- Email: [email protected]
Service assurance packet-scheduling algorithm for a future aeronautical mobile communication system
Journal Pre-proof
Service assurance packet-scheduling algorithm for a future aeronautical mobile communication system Kazuyuki Morioka , Xiaodong Lu , Junichi Naganawa , Norihiko Miyazaki , Naruto Yonemoto , Yasuto Sumiya , Akiko Kohmura PII: DOI: Reference:
S1569-190X(19)30190-X https://doi.org/10.1016/j.simpat.2019.102059 SIMPAT 102059
To appear in:
Simulation Modelling Practice and Theory
Please cite this article as: Kazuyuki Morioka , Xiaodong Lu , Junichi Naganawa , Norihiko Miyazaki , Naruto Yonemoto , Yasuto Sumiya , Akiko Kohmura , Service assurance packet-scheduling algorithm for a future aeronautical mobile communication system, Simulation Modelling Practice and Theory (2019), doi: https://doi.org/10.1016/j.simpat.2019.102059
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
Service assurance packet-scheduling algorithm for a future aeronautical mobile communication system Kazuyuki Morioka*, Xiaodong Lu, Junichi Naganawa, Norihiko Miyazaki, Naruto Yonemoto, Yasuto Sumiya, and Akiko Kohmura National Institute of Maritime, Port and Aviation Technology, Electronic Navigation Research Institute (MPAT/ENRI) 7-42-23, Jindaiji-higashimachi, Chofu, Tokyo 182-0012, Japan Abstract In recent years, realizing safe and efficient air traffic management (ATM) has become one of the most important social issues because of the rapid increase in air traffic volume. To realize safe and efficient ATM, it is necessary for related stakeholders to share aeronautical big data by connecting to aircrafts and ground systems efficiently and globally. However, the current aeronautical communication systems are not adapted to be used in support of global collaborative decision making (CDM) through information exchange. In order to accelerate the implementation of information collaborative environment and improve operational awareness in the aeronautical domain, this study focuses on an information platform for sharing aeronautical big data, and a communication medium for achieving Internet of aeronautical things. This paper proposes a new packet-scheduling algorithm for a next-generation aeronautical mobile communication system that includes various types of services and devices. The algorithm balances service assurance and service fairness using strict priorities for real-time services and loose priorities for non-real-time services. Performance evaluations were conducted using a network simulator to confirm the effectiveness of the proposed algorithm. First, a simple single-device scenario was utilized to confirm the effectiveness of the quality of service function for an aeronautical mobile communication system. Second, a multiple-device scenario was employed to evaluate the performance of the proposed scheduler. Finally, an airport scenario was used to demonstrate the effectiveness of our algorithm in a more practical situation. Furthermore, we show that the airport scenario is useful for evaluating the overall performance of a new aeronautical mobile communication system in the system introduction phase or cell design phase.
Keywords: Aeronautical big data, System Wide Information Management (SWIM), Aeronautical IoT, Aeronautical Mobile Airport Communications System (AeroMACS), orthogonal frequency-division multiple access (OFDMA), quality of service (QoS), packet-scheduling algorithm ------------------------------------------------*
Corresponding author
Email address: [email protected] 1. Introduction In recent years, realizing safe and efficient air traffic management (ATM) has become one of the most important social issues because of the rapid increase in air traffic volume. To realize safe and efficient ATM, it is necessary for aeronautical stakeholders, such as pilots, air traffic controllers, airline operators, and airport operators, to share big data related to aircraft operations efficiently and globally. (A’) Nowadays, big data analytics, cloud computing, and IoT have been receiving considerable attention to create new services in several industrial sectors. In the aviation sector, there has also been a growing interest in AI and big data analytics for decision making based on aeronautical information shared by IoT and the cloud. For example, critical decision making and predictive analytics for trajectory management are studied in [1]. Furthermore, mobile edge computing for air-to-ground integration systems, which are advanced versions of aeronautical IoT, is discussed in [2]. However, the current ATM information systems face difficulties in terms of system integration because they are based on different data formats and exchange protocols. To solve this issue, the International Civil Aviation Organization (ICAO) has standardized a future aeronautical information-sharing platform, called System Wide Information Management (SWIM) [3]. Using SWIM, efficient system integration can be realized by sharing aeronautical big data, such as flight, weather, and surveillance data using the standardized data formats and exchange protocols. To realize SWIM, communication between moving aircraft and fixed ground systems is necessary, and an effective aeronautical mobile communication system is important for this purpose. However, the current aeronautical mobile communication system, i.e., VHF datalink mode 2, was developed decades ago and has a maximum transmission rate of only 31.5 kbps. In addition, it is not compatible with the common internet protocol (IP) and has security problems because it is not encrypted. Therefore, it is difficult for current aeronautical mobile communication system to realize SWIM. To solve these issues, the ICAO has also standardized a future aeronautical communication system, called the Aeronautical Mobile Communication System (AeroMACS) [4–6]. AeroMACS is based on the IEEE802.16 standard [7] for use on the airport surface and enables rich information sharing via image or video transmission through a broadband wireless channel. In addition, the AeroMACS is compatible with IP, thereby reducing the system introduction and application development costs. Furthermore, it is more secure than the conventional aeronautical mobile communication system, because it is encrypted in the multiple protocol layers. These advantages make it promising medium to realize aeronautical IoT. Since the IEEE802.16 standard is targeted at public broadband wireless access, it offers best
effort (BE) services; consequently, the more users are in the serving cell, the worse the service quality becomes. On the other hand, AeroMACS was developed for air traffic control services (ATS), which necessitate high, stable quality due to the stringent aircraft safety requirements. Fig. 1 illustrates the usage of AeroMACS around an airport. Aeronautic communication can be classified as ATS communication or airline operational control (AOC) communication based on its content. In particular, ATS communication, which includes air traffic control messages in the form of analog/digital voice communication between pilots and air traffic controllers, is the most important
Fig. 1. Multi-media services using AeroMACS at an airport. type of communication for aircraft safety. In addition, controller–pilot data link communication (CPDLC), which is a typical data exchange service for air traffic control, is categorized as ATS communication. Meanwhile, AOC communication includes flight plan data exchanged among the staff of each airline to support their aircraft operations. Other than the data falling into the ATS and AOS categories, there are aircraft surveillance data obtained by aeronautical radar; automatic terminal information service (ATIS) data, which include information about the weather around the airport and runway status information; and so on. Thus, aeronautical communication systems must handle different types of information with various importance levels. In addition to the different types of information content, there are various types of user devices in aeronautical mobile communication systems. Aircraft are the largest of these user devices, as well
as having the highest speeds among these devices and difficulty turning in small radii; therefore, assistance from outside the aircraft via a wireless channel is necessary. The vehicles driving in the airport constitute the second most important type of user devices. These vehicles include aircraft-towing cars, cargo carriers, security cars, refueling cars, and airline buses that carry passengers or crews to/from the aircraft. The drivers can recognize the surroundings of their vehicles by themselves, so the importance of communication on these vehicles is lower than that on aircraft. The importance of the devices carried by airport workers and fixed sensor devices such as weather sensors and security video cameras is lower than that of the aircraft and vehicles. In this research, we consider SWIM as a platform for aeronautical big data, and AeroMACS as a medium for aeronautical IoT. This concept is referred to as aircraft access to SWIM (AAtS) over AeroMACS and was firstly proposed in [8,9]. We developed prototype SWIM [10–12] and AeroMACS [13–17] systems, then conducted field trials of AAtS [18]. (C’) In [19], we presented the results of practical testing for AAtS on the airport and demonstrated the effectiveness of quality of service (QoS) function in AeroMACS. The scenario used only one user’s device for proof of the concept. In the practical application, several devices will access the system and use various types of services simultaneously under different conditions. In this situation, network simulation is considerably more effective than practical test in terms of cost and resource. In this paper, we consider the cases which include several devices and various services simultaneously. Moreover, a scheduling algorithm for AeroMACS is proposed and evaluated in this paper. (B’) Furthermore, when numerous devices access the system and use various services simultaneously, the QoS function plays an important role. To assure total service quality, the QoS control has to be considered in multiple layers adequately. QoS control in the application layer is crucial, as it is the closest part to the end user. A task scheduling method in heterogeneous clouds was studied in [20]. The authors stated that traditional single layered scheduling approaches do not work and multi-layered approach should be considered in a heterogeneous environment. They proposed two multi-criteria meta-heuristic algorithms for scheduling optimization of bag-of-tasks (BoT) applications on an interlinked cloud system. The idea that uses the multi-layered approach in the heterogeneous environment is reasonable, and we also consider the multi-layered scheduling algorithm for radio resource management problems in this paper. In [21], the workflow scheduling problem from the QoS and data locality perspectives in large-scale distributed systems were investigated. They proposed a scheduling approach, considering synchronization models for the tasks in a work-flow application. Furthermore, the impact of workload variability was investigated in terms of computational volume and interarrival times in [22]. They employed four heuristics for the scheduling of the workload to evaluate the system performance in terms of QoS. These studies considered QoS in the application layer.
In addition to QoS control in the application layer, QoS control in the communication layer is also important to assure the total QoS that is proposed and discussed in this paper. In particular, priority handling for the radio channel, which is the most congested in the end-to-end communication pass, is essential in SWIM access that involves various types of services and devices. Therefore, a study of the QoS function for AeroMACS is very important for SWIM access. There are several papers related to the QoS function for IEEE 802.16 [23–28]. However, these studies were mainly focused on public communication; therefore, all devices were treated as equal and inter-device QoS was not considered. This paper proposes a new packet-scheduling algorithm to ensure that QoS requirements are met by future aeronautical mobile communication systems that include various types of services and devices. Several QoS requirements are described in the AeroMACS technical manual [5]; however, the implementation method is left for the vendors to determine. Therefore, the packet-scheduling algorithm, which is a concrete implementation method of the QoS function is a very important practical issue. In addition, the packet scheduling problem, i.e., the radio resource allocation problem in orthogonal frequency-division multiple access (OFDMA), which is a multiple access method for AeroMACS, is a kind of packing problem in the area of discrete mathematics. Thus, the packet-scheduling algorithm is an important problem not only practically, but also academically. The rest of this paper is organized as follows. Section 2 introduces the system model, the problem formulation, and several packet-scheduling algorithms for IEEE802.16. Section 3 proposes the new packet-scheduling algorithm for AeroMACS, and Section 4 describes the network simulations performed to evaluate the effectiveness of the proposed algorithm and their results. Finally, Section 5 concludes this paper.
2. System model As AeroMACS is based on the IEEE802.16 standard [7], the IEEE802.16 system model is described here. The standard is a connection-oriented system, so the subscriber station (SS) sets up a connection to the base station (BS) before the services are started. The BS allocates a connection ID (CID) to each connection; then, each connection is associated with a service flow (SF).
Fig. 2. (a) Packet scheduler in BS and (b) radio frame structure of the IEEE802.16 standard. The downlink traffic from the application layer is classified by CID, then transferred to the appropriate SF queue according to its service policy. Next, the packet scheduler in the BS decides which packet should be transmitted to the wireless channel first to meet the QoS requirements. Fig. 2(a) shows the packet scheduler in the BS. Fig. 2(b) shows the radio frame structure of the IEEE802.16 standard. The vertical axis represents the frequency domain, called subcarriers in orthogonal frequency-division multiplexing (OFDM), and the horizontal axis indicates the time domain, called symbols in OFDM. As shown in the figure, the OFDMA frame can be seen as a rectangular logical structure consisting of K subcarriers and S symbols. The BS allocates to each SS a sub-region, called a burst, which consists of k consecutive subcarriers and s consecutive symbols. Then, the packet scheduler packs the packets from the upper layer into the burst. The packet scheduler is implemented in the medium access control layer in the BS. The problem of allocating appropriate bursts to each SS to maximize the total throughput, while meeting the QoS requirements, is called a packet scheduling problem.
2.1. QoS definitions in IEEE802.16 standard The QoS definitions described in the IEEE802.16 standard are summarized in this section. The following service types are defined in the IEEE802.16 standard and are followed by AeroMACS:
Unsolicited grant service (UGS): It supports constant bitrate (CBR) or fixed-throughput connections such as voice over IP
(VoIP). This service guarantees that the throughput, latency, and jitter will be of the necessary levels.
Extended real-time polling service (ertPS): It supports CBR or fixed-throughput connections, like UGS. The difference from UGS is that
UGS allocates resources even in silence or no-packet periods, while ertPS does not allocate resources in such periods. This class is an intermediate class between UGS and real-time polling service (rtPS). It improves the overhead of UGS and the delay of rtPS simultaneously.
rtPS: It supports real-time variable bit rate (VBR) traffic such as video streaming services via
minimum reserved and maximum sustained traffic rates and requires tolerably stringent latency constraints.
Non-real-time polling service (nrtPS): It supports delay-tolerant streams with variably sized packets, for which only minimum
reserved and maximum sustained traffic rates are required, such as FTP. These applications are time-insensitive and require minimum bandwidth allocation.
BE service: It supports services such as HTTP and email. BE services are handled on a space-available
basis and do not require stringent latency/jitter constraints.
2.2. Problem statement The packet scheduling problem to maximize the total throughput while meeting the QoS requirements for each service type described in Section 2.1 can be defined as the following constrained optimization problem: 𝐿
𝐾
𝑆
1 arg max ∑ ∑ ∑ 𝑟𝑖 ,𝑘, 𝑠-𝜌𝑖 ,𝑘, 𝑠- , 𝜌 𝑇
(1)
𝑖=1 𝑘=1 𝑠=1
subject to 𝐾
𝑆
1 ∑ ∑ 𝑟𝑖 ,𝑘, 𝑠-𝜌𝑖 ,𝑘, 𝑠- = 𝑅𝑖max , ∀𝑆F ∈ *UGS+ 𝑇
(2)
𝑑𝑖 ≤ 𝐷𝑖 , ∀SF ∈ *UGS, ertPS, rtPS+
(3)
𝑘=1 𝑠=1
𝐾
𝑅𝑖min ≤
𝑆
1 ∑ ∑ 𝑟𝑖 ,𝑘, 𝑠-𝜌𝑖 ,𝑘, 𝑠- ≤ 𝑅𝑖max , ∀𝑆F ∈ *ertPS, rtPS, nrtPS+ 𝑇
(4)
𝑘=1 𝑠=1
𝜌𝑖 ,𝑘, 𝑠- ∈ *0,1+, ∀𝑖, 𝑘, 𝑠
(5)
𝐿
∑ 𝜌𝑖 ,𝑘, 𝑠- = 1, ∀𝑘, 𝑠,
(6)
𝑖=1
where i, k, and s are the indices representing the connection, subcarrier, and symbol, respectively, and L, K, and S are the total numbers of connections, subcarriers, and symbols, respectively. 𝑟𝑖 ,𝑘, 𝑠shows the maximum number of bits per symbol, which can be transmitted by the i-th connection using the k-th subcarrier and s-th symbol. 𝜌𝑖 ,𝑘, 𝑠- is the allocation index of the i-th connection for the k-th subcarrier and s-th symbol. Hence, if 𝜌𝑖 ,𝑘, 𝑠- = 1, then the corresponding resource is allocated to the i-th connection. Eqs. (5) and (6) indicate that one resource can be allocated to only one connection. 𝐷𝑖 is the allowable maximum delay for a real-time service. 𝑅𝑖min and 𝑅𝑖max are the minimum reserved traffic rate and maximum sustained traffic rate, respectively. Finally, T represents the duration of an OFDM frame. Obtaining the optimal solution of Eqs. (1)–(6) is difficult, because there are many parameters to consider. Therefore, several algorithms that are sub-optimal but easy to implement were proposed in [23–28]. This two-dimensional rectangular mapping problem is a kind of the bin packing problem known as an NP-hard problem. Thus, the problem considered in this paper is important not only practically, but also academically.
2.3. Scheduling algorithm for IEEE802.16 standard In the previous section, we described the packet scheduling problem considered in this paper. Next, we review some of the scheduling algorithms that have been proposed so far. The simplest one is the round robin (RR) scheduler. The RR scheduler allocates radio resources to SSs in order equally, without considering their priority. This scheduler is simple and easy to implement; however, it is inappropriate for systems that have different levels of priorities and those in which the packet size varies frequently. The weighted RR (WRR) scheduler [29], which is an extended version of the RR scheduler, allocates a kind of weight to each SF. Then, the scheduler distributes resources based on the weight, which is adjusted according to the QoS requirements. The deficit RR (DRR) scheduler [30] associates a deficit counter to each SF. Then, the scheduler distributes resources based on the deficit counter, which is adjusted according to the QoS requirements. The WRR and DRR schedulers belong to the same category. The WRR and DRR schedulers were previously applied as uplink and downlink packet schedulers, respectively, for the IEEE802.16 standard [23,24]. These algorithms are focused on throughput assurance; therefore, they are appropriate for non-real-time services. For real-time services, the earliest deadline first (EDF) [31] scheduler, which allocates resources to the SS that has the first deadline, is adequate. The algorithms mentioned above target single service and do not consider the priorities of
multiple services. To ensure sufficient QoS among multiple services that have different levels of importance, hierarchical scheduling algorithms were proposed in [25–28]. Kitti and Aura [25] and Niell [26] applied two layered-hierarchical schedulers separately to the uplink and downlink for the IEEE802.16 standard. The incoming packets are allocated to a priority queue according to the service class in the first layers of these algorithms. Here, strict priorities are applied; hence, the most important priority queue is served first, and the second most important priority queue is served when the first queue becomes empty. UGS has the highest priority, followed sequentially by ertPS, rtPS, nrtPS, and BE services. In the second layers of these algorithms, the different schedulers are applied to each SFs to meet the QoS requirements. Fixed allocation is applied for UGS, the WRR scheduler is adapted for ertPS and rtPS, and the RR scheduler is used for BE services. The drawback of these algorithms is that the low priority queue is not served when the high priority queue is always busy. To solve this problem, deficit fair priority queue (DFPQ)-based second layer hierarchical scheduling algorithms were proposed in [27,28]. The DFPQ loosens the inter-service priority order in the first layer using a deficit counter. The deficit counter is decreased according to the allocated packet size. The next most important queue is served when the counter becomes zero. In the second layer, different schedulers are applied to each SF to meet the QoS requirements, as in [25,26]. The DFPQ-based algorithms are superior in terms of fairness for public broadband wireless access.
(D’) 2.4. QoS control for aeronautical telecommunication network Aeronautical telecommunication network (ATN) has been developed by ICAO to integrate air-ground and ground-ground data communications for air traffic control and aeronautical operational communications. QoS control plays an important role in ATN, to carry time critical information required for aircraft safety. In ATN, giving priority has the essential role to ensure that high priority safety and time critical data are not delayed by low priority non-safety data [32]. QoS control and the scheduling algorithm for satellite communication system are discussed in [33, 34]. Furthermore, QoS control for aeronautical ad hoc network (AANET) is studied in [35, 36]. A radio frame scheduling algorithm for time division multiple access was proposed in [35]. A multiple QoS parameters-based routing protocol (MQSPR) was studied in [36], to improve the overall network performance for communication between the aircraft and the ground. This paper considers AeroMACS. As noted in [6], scheduling and resource management algorithms are not specified in the AeroMACS standard, and hence, thus far, they have been open issues requiring additional research efforts. A resource allocation algorithm for AeroMACS was discussed for increasing the communication system capacity using the tap correlation related to each
user communication channel, which is a typical feature of an airport propagation model [37]. The algorithm aimed for maximizing communication rate utilizing channel information. However, it did not consider QoS parameters defined in AeroMACS standard [5]. Our proposed algorithm balances service assurance and service fairness considering QoS parameters defined in [5], while adapting strict priorities for real-time services and fair priorities for non-real-time services.
3. Proposed method Some packet-scheduling algorithms for the IEEE802.16 standard were summarized in the previous section. As the hierarchical scheduling algorithm is superior for ensuring that the QoS requirements are met for various SFs, we also adopted a hierarchical structure in our scheduling algorithm for AeroMACS. The difference between IEEE802.16 and AeroMACS is that IEEE802.16 is targeted at public access in which users are treated equally, while AeroMACS is targeted at aeronautical use, which has various types of user devices: aircraft, vehicles, sensors, and so on. In this section, additional requirements for AeroMACS are described firstly; then, a new packet-scheduling algorithm for AeroMACS is proposed.
3.1. Device class The SSs in AeroMACS are equipped in various devices such as aircraft, vehicles, and sensors. Therefore, new QoS classes are defined in the AeroMACS technical manual as follows:
Aircraft – the modems installed in aircraft. Considering both safety and non-safety applications, all identified types of SFs are recommended for aircraft.
Vehicle – devices hosted on all ground support vehicles, including passenger vans/buses, dollies for carrying cargo, refueling trucks, catering vehicles, push-back tugs/tractors, etc.
Ground critical – fixed devices used to monitor/control ground equipment that are deployed for critical ATS services, such as radar, landing systems, runway lighting controls, etc.
Ground default – all other ground fixed or nomadic equipment supporting the safety and regularity of flight operations.
Table 1 shows the QoS parameters described in the AeroMACS technical manual. VoIP in this table represents digital VoIP and indicates that UGS or ertPS should be used for VoIP. NET shows the management data for an ATM network (ATN), and ATS and AOC represent communications for air traffic control services and airline operational services, respectively. This table demonstrates that
rtPS should be used for NET, ATS1, and ATS2, while nrtPS should be used for ATS3 and AOC1. DEFAULT shows that services other than ATS/AOC, such as sensor data, and BE services should be used for the DEFAULT service classes. MSTR represents the maximum sustained traffic rate, which defines the maximum data rate that can be allocated for the corresponding connection. MRTR is the minimum reserved traffic rate, which defines the minimum data rate that must be allocated for the corresponding connection. Finally, ML indicates the maximum latency, which is the maximum allowable delay.
Table 1 QoS parameter values in the AeroMACS downlink SFs [5]. Service
Service
Class
Type
QoS Parameters
Mandatory Services for Each Device Type
MSTR
MRTR
ML
(kbps)
(kbps)
(s)
Aircraft
Vehicle
Ground
Ground
Critical
Default
X
VoIP1
UGS (ertPS)
64
64
0.15
X
X
NET
rtPS
34
32
1
X
X
X
ATS1
rtPS
34
32
1.5
X
X
X
ATS2
rtPS
34
32
1.5
X
ATS3
nrtPS
34
32
N/A
X
AOC1
nrtPS
67
64
N/A
X
DEFAULT
BE
N/A
N/A
N/A
X
X
X
X
Although the QoS requirements are described in detail in Table 1, the concrete means of implementation, such as the packet-scheduling algorithm, are left for the vendor to decide. Therefore, we propose a new packet-scheduling algorithm for AeroMACS in the next section.
3.2. Scheduling algorithm for AeroMACS In this section, we propose a new packet-scheduling algorithm for AeroMACS to meet the requirements described in Section 3.1. Fig. 3 provides an overview of the proposed algorithm, which consists of three-layered hierarchical schedulers. This algorithm balances service assurance and fairness using strict priorities for the real-time services and loose priorities for the non-real-time services.
Fig. 3. Overview of the proposed algorithm.
Fig. 4. Weight model for the proposed algorithm. In the first layer, our algorithm schedules the service types based on strict priorities, because communication delays in the ATM may impair the safety of flight operation. UGS, ertPS, and rtPS, which have stringent delay requirements, are scheduled first in that order. Then, nrtPS and BE, which are non-real-time services, are scheduled in that order.
In the second layer, our algorithm schedules the device class. As noted in Section 3.1, the device classes newly defined in the AeroMACS technical manual are aircraft, vehicle, ground critical, and ground default. The aircraft are the largest devices, have the highest speeds, and have difficulty turning in small radii; therefore, assistance from outside the aircraft via a wireless channel is necessary. Thus, the aircraft should have the highest priority. In our scheduler, a strict priority queue based on device class is used for real-time services such as UGS, ertPS, and rtPS, which have stringent delay requirements. The aircraft have the highest priority, followed sequentially by vehicles, ground critical devices, and ground default devices. This order ensures high QoS for the real-time services of the aircraft. On the other hand, a fair scheduler is used for the non-real-time services such as nrtPS and BE, to prevent the high priority services from occupying the resources. Finally, in the third layer, our algorithm uses fixed allocation scheduler for the UGS and ertPS, the EDF scheduler for the rtPS, the WRR scheduler for the nrtPS, and the RR scheduler for BE. (G’) Fig. 4. illustrates the weight model in real-time/non real-time and critical/non critical axes. The numbers in the figure represent the weight-level for each service. The smaller numbers indicate high weight, and the larger numbers indicate low weight. Fig. 5 shows the proposed algorithm for AeroMACS. Proposed algorithm is an extension of a two-layer scheduling algorithm to a three-layer one and can be seen as a combination of strict priorities [25,26] and loose priorities [27,28]. The third layer exists for at most three services: UGS, ertPS, and rtPS. In addition, there are only four device classes. Therefore, the increase in computational complexity is limited.
4. Performance evaluation A new packet-scheduling algorithm for the AeroMACS was proposed in the previous section. Here, we describe the evaluation of the effectiveness of the algorithm, which was performed using the ns3, an event-driven network simulator that is widely used in academic research [38]. The ns3 simulator includes the WiMAX module [39,40], and we implemented our packet scheduler using this module. We also implemented an automatic modulation and coding (AMC) function, which adaptively selects the appropriate modulation and coding scheme according to the channel status. Firstly, a simple single-device scenario was utilized to confirm the effectiveness of the QoS function in the aeronautical mobile communication system. Secondly, a multiple-device scenario was employed to evaluate the performance of the proposed scheduler. Finally, an airport scenario was used to show the effectiveness of our method in a more practical situation. Furthermore, we demonstrated that the airport scenario is useful for evaluating the overall system performance in the system introduction or cell design phase.
4.1. Simulation setup The simulation setup is described in this section. In our simulation, the received signal strength
fluctuated according to the movement of the SSs. Then, the AMC was adopted to follow the channel fluctuations automatically. The propagation model was the free space loss model, which is the simplest model. Furthermore, symbol base allocation was used to assign a radio frame. Hence, the number of sub-carriers was fixed to K, and the two-dimensional allocation problem was reduced to a one-dimensional allocation problem. This restriction did not affect the evaluation results for the packet-scheduling algorithm.
Fig. 5. Proposed scheduling algorithm for AeroMACS.
Table 2 Simulation parameters. Traffic Pattern for Aircraft SSs Service
Service Type
Size (bytes)
Interval (ms)
Throughput (kbps)
VoIP1
UGS
160
20
64
NET
rtPS
400
100
32
ATS1
rtPS
400
100
32
ATS2
rtPS
400
100
32
ATS3
nrtPS
400
100
32
AOC1
nrtPS
800
100
64
DEFAULT
BE
800
100
64
Class
Traffic Pattern for Vehicle SSs Service
Scheduling Type
Size (bytes)
Interval (ms)
Throughput (kbps)
VoIP1
UGS
160
20
64
NET
rtPS
400
100
32
ATS1
rtPS
400
100
32
DEFAULT
BE
800
100
64
Class
Traffic Pattern for Ground Default SSs Service
Scheduling Type
Size (bytes)
Interval (ms)
Throughput (kbps)
NET
rtPS
400
100
32
DEFAULT
BE
800
100
64
Class
Table 2 shows the traffic patterns for the SSs used in the simulation, which were determined based on the QoS requirements shown in Table 1. Each aircraft device used seven services. VoIP1 assumed voice service between the pilot and air traffic controller and sent 160-byte packets in 20 ms intervals. NET assumed management packets in an ATN, and ATS1, ATS2, and ATS3 assumed data services related to ATC, such as CPDLC. NET, ATS1, ATS2, and ATS3 sent 400-byte packets in 100 ms intervals. AOC1 assumed data communication for airline operation. DEFAULT assumed data communication other than ATS/AOC. AOC1 and DEFAULT sent 800-byte packets in 100 ms intervals. The total throughput per aircraft was 320 kbps. The vehicle devices used four services: VoIP, NET, ATS1, and DEFAULT. The total throughput per vehicle was 192 kbps. The ground default devices used NET and DEFULT. The total throughput per ground default device was 96 kbps. Ground critical devices were not included in our scenarios for simplicity.
4.2. Single-device-class scenario Using the setup described in the previous section, we conducted simulations for various scenarios. Firstly, a simple single-device-class scenario was utilized to confirm the effectiveness of the QoS function in the aeronautical mobile communication system. In this scenario, we assumed that there were aircraft devices only and compared the performance between the QoS-enabled and QoS-disabled cases while increasing the number of aircraft devices. Here, we assumed that the aircraft did not move and that the signal-to-noise ratio (SNR) was constant. Therefore, the modulation format did not change. It is set to 64QAM with 3/4 rate convolutional coding. Fig. 6 compares the throughput between the QoS-enabled case (a) and QoS-disabled case (b). The throughput here is the total number for the same service type. Firstly, we compare UGS (red line in the figure), which is the most urgent service type, between the two cases. In the QoS-enabled case, the UGS throughput reaches the limit when the number of aircraft devices becomes 20. On the other hand, the UGS throughput increases as the number of the aircraft devices increases in the QoS-disabled case.
Fig. 6. Throughput comparison for each service type. Next, we compared nrtPS and BE services, which are non-real-time services. In the QoS-enabled case, the throughputs decrease when the number of aircraft devices exceeds 20. On the other hand, the throughputs of nrtPS and BE services are constant when the number of aircraft devices exceeds 20 in the QoS-disabled case. These results demonstrate that the UGS, which has higher priority, was prioritized over nrtPS and BE services, which have lower priorities, when the QoS function was enabled, confirming the effectiveness of the QoS function. Fig. 7(a) shows the total throughput of the system. The reason that the throughput reaches the limit of about 6 Mbps is that the channel bandwidth of the AeroMACS per channel is 5 MHz;
therefore, the maximum throughput becomes about 7.5 Mbps. In addition, some protocol overhead is included, such as protocol headers and control packets. Then, the maximum throughput of the system becomes about 6 Mbps per channel.
Fig. 7. Comparison of total throughput and total packet loss rate between the QoS-enabled and QoS-disabled cases.
From Fig. 7(a), we can see that the total throughput in the QoS-enabled case becomes lower than that in the QoS-disabled case when the number of aircraft devices exceeds 20. The reason for this behavior is that the number of UGS packets, which were small, increased as the prioritization and then the relative overhead increased. Fig. 7(b) shows the total packet loss rate of the system. From this figure, we can see that the packet loss rate in the QoS-enabled case is lower than that in the QoS-disabled case. The reason for this difference is that the packet loss rate of the UGS improved dramatically. Note that the transmission interval of the UGS is the shortest, at 20 ms, and the number of packets transmitted per unit time becomes the largest. Therefore, the impact of the UGS on the total packet loss rate becomes large. The results in Fig. 7(b) also show the effectiveness of the QoS function in the aeronautical mobile communication system.
4.3. Multiple-device-class scenario A new packet-scheduling algorithm was proposed in Section 3.2. To confirm the effectiveness of this algorithm, a multiple-device-class scenario was employed, as described in this section. We compared our three-layered algorithm to the two-layered algorithm proposed in [25,26]. In this scenario, there were 15 aircraft devices and 20 ground default devices. Then, we evaluated the throughput and packet loss rate when the number of vehicle devices increased. The traffic pattern of
each device is described in Table 2. We assumed that the aircraft did not move and that the SNR was constant. Therefore, the modulation format did not change. We used 64QAM with 3/4 rate convolutional coding, as in the previous scenario. Figs. 8(a–c) compare the throughput for each service type. Here the throughput is the total value for each device class. The dotted and solid lines show the results obtained using the existing and proposed algorithms, respectively.
Fig. 8. Comparison of the throughput for each device class. Firstly, we focus on the UGS and rtPS of the aircraft and vehicle devices. From Fig. 8(b), we can see that the throughput of the aircraft devices obtained using the existing algorithm decreases rapidly when the number of vehicle devices exceeds 35; however, it is almost constant when the proposed algorithm is used. In contrast, Fig. 8(a) shows that the throughput of the vehicle devices obtained using the proposed algorithm is lower than that resulting from using the existing algorithm when the number of vehicle devices exceeds 35. These results demonstrate that the real-time service of the aircraft devices is prioritized over the UGS of the vehicle devices, which implies that the
proposed algorithm worked effectively. Next, we focus on the rtPS of the vehicle and ground default devices. From Fig. 8(b), we can see that the throughput of the vehicle devices obtained using the proposed algorithm is higher than that resulting from using the existing algorithm when the number of vehicle devices is between 20 and 35. On the other hand, the throughput of the ground default devices is lower than that obtained using the existing algorithm in the same range. These results demonstrate that the vehicle devices are prioritized over the ground default devices, which have lower priority than the vehicle devices, again indicating the effectiveness of our proposed algorithm. Fig. 8(d) shows the total throughput for each device class, demonstrating that the real-time service of the vehicle devices is prioritized over that of the ground default devices when the number of vehicles is between 20 and 35, while the real-time service of the aircraft devices is prioritized over that of the vehicle devices when the number of vehicle devices exceeds 35. These results confirm the effectiveness of our proposed algorithm for AeroMACS.
4.4. Practical airport scenario Finally, a practical airport scenario was utilized to confirm the effectiveness of our proposed algorithm in an airport use case. In this scenario, the SSs moved and SNR fluctuated. Therefore, the modulation and coding scheme changed adaptively. The simplest free space propagation loss model was used. The total number of aircraft devices was 15. One of them was about to take-off at a speed of 300 km/h on runway, another was taxiing at 60 km/h on taxiway, and another was waiting for take-off on the edge of the runway. The remaining 12 aircraft were parked on the apron. The total number of vehicle devices was set to 30, 40, and 50. Thirty vehicles were moving on the surrounding road at 30 km/h, and the rest were parked in random positions, but not on the runway or taxiway. The total number of ground default devices was 20, and they were fixed at random positions around the airport, but not on the runway or taxiway. The traffic model is described in Table 2 and the ground critical device is not used as the same as previous two scenarios. The length of the runway is 3 km. The transmit power of the BS is 23 dBm and the antenna gain and antenna height of the BS are 11 dB and 30 m, respectively. The antenna gain of the SS is 0 dB. The antenna heights of the aircraft device, vehicle device, and ground default device are 10 m, 2 m, and 1 m, respectively. Fig. 9 shows a screen shot of the ns3 visualization tool called Netanim [41] that was obtained while conducting the simulation described above. At this moment, a packet is being transmitted to the aircraft device parking on the apron. We can see the movement of the SS and packet flow visually.
Fig. 9. Screen shot of Netanim for a practical airport scenario. Fig. 10 shows a comparison of the jitter for the rtPS flow of an aircraft device, as the number of vehicle devices increases from 30 to 50. The jitter is defined by the average fluctuation from the average delay time. From this figure, we can observe that the jitter becomes large as the number of vehicles increases when the existing algorithm is used. This result implies that the existing algorithm does not consider inter-device priority; therefore, the real-time service of the aircraft device is affected by the service of the vehicle devices.
Fig. 10. Comparison of jitter for the rtPS flow of an aircraft device.
On the other hand, the jitter obtained using the proposed algorithm does not increase even though the number of vehicle devices increases, because the real-time service of the aircraft device is not affected by the other devices when the three-layered scheduling algorithm and inter-device priority handling are introduced. These results again confirm the effectiveness of our proposed algorithm. Finally, Fig. 11 shows the results of detailed analysis performed using Wireshark [42], which is a common packet analyzer. From this figure, we can see that the important services of the aircraft device were continued without decreasing the throughput. As shown in this example, the simulation tools and scenarios employed in this study are useful for service design, cell design, and antenna placement design in the system introduction phase.
Fig. 11. Results of detailed packet analysis performed using Wireshark [42].
5. Conclusions In this paper, we proposed a new packet-scheduling algorithm for a next-generation aeronautical mobile communication system that involves various types of services and devices. The proposed algorithm consists of three-layered hierarchical schedulers and balances the service assurance and service fairness by using strict priorities for the real-time services and loose priorities for the non-real-time services. Performance evaluations were conducted using a network simulator to confirm the effectiveness of the proposed algorithm. Firstly, a simple single-device scenario was employed to confirm the effectiveness of the QoS function in an aeronautical mobile communication system. Secondly, a multiple-device scenario was
utilized to evaluate the performance of the proposed scheduler. Finally, an airport scenario was used to demonstrate the effectiveness of our method in a more practical situation. Furthermore, we showed that the airport scenario was useful for evaluating overall system performance in the system introduction or cell design phase of a future aeronautical mobile communication system. Although only the downlink channel was considered in this study, our algorithm can be extended naturally to the uplink channel. Evaluation in an environment with co-existing downlink and uplink channels is left as future work, as is the study of multicast services that can be used for ATIS, aeronautical enroute information service (AEIS), and so on.
Acknowledgements A part of this work was supported by the Japan Civil Aviation Bureau (JCAB) for the R&D project on an advanced air-to-ground aeronautical communication system.
References [1] A. Samet, P. Johnathan, C. Paul, S. David, B. Steve, G. Gary, Predictive analytics with aviation big data, in: IEEE International Conference on Integrated Communications, Navigation and Surveillance Conference (ICNS), 2013. [2] Z. Zhenyu, F. Junhao, T. Lu, H. Yejun, G. Jie, An air-ground integration approach for mobile edge computing in IoT, IEEE Communications Magazine 56 (2018) 40–47. [3] Manual on System Wide Information Management (SWIM) Concept, ICAO Doc 10039. [4] AeroMACS Standards and Recommended Practices (SARPs), ICAO Annex 10, Vol. III, Ch. 7, 2016. [5] Manual on the Aeronautical Mobile Airport Communications System (AeroMACS), ICAO Doc 10044, 2017. [6] G. Bartoli, R. Fantacci, D. Marabissi, AeroMACS: A new perspective for mobile airport communications and services, IEEE Wireless Commun. 20 (2013) 44–50. [7] IEEE std 802.16-2009, Part 16: Air Interface for Broadband Wireless Access Systems, IEEE, 2009. [8] C. Antonio, A. Rafael, Aircraft access to SWIM (AAtS) for airport surface operations: A system design methodology, in: Proceedings of the Integrated Communication, Navigation, and Surveillance Conference (ICNS), 2016. [9] R. Apaza, B. Abraham, T. Maeda, AAtS over AeroMACS technology trials on the airport surface, in: Proceedings of the Integrated Communication, Navigation, and Surveillance Conference (ICNS), 2016.
[10] X. Lu, T. Koga, System wide information management for heterogeneous information sharing and interoperability, in: Proceedings of the IEEE 13th International Symposium on Autonomous Decentralized Systems, 2017, pp. 199–204. [11] X. Lu, K. Morioka, T. Koga, Y. Sumiya, Collaborative flight and flow information exchange to achieve seamless air traffic management operation, in: Proceedings of the IEEE 13th International Symposium on Autonomous Decentralized Systems, 2019. [12] X. Lu, K. Morioka, T. Koga, Y. Sumiya, Air-ground system wide information management to achieve safe flight operation, in: Proceedings of the IEEE International Symposium on High Assurance Systems Engineering, 2019. [13] K. Morioka, N. Kanada, S. Futatsumori, A. Kohmura, T. Tomita, N. Yonemoto, Y. Sumiya, Basic characteristic evaluation of aeromacs prototype system in Sendai airport, in: 2015 Integrated Communication, Navigation and Surveillance Conference (ICNS), 2015. [14] K. Morioka, J. Naganawa, J. Honda, N. Kanada, S. Futatsumori, A. Kohmura, T. Tomita, N. Yonemoto, Y. Sumiya, Experimental evaluation on quality of VoIP over Aero MACS, in: 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), 2016. [15] J. Naganawa, K. Morioka, N. Kanada, J. Honda, T. Tomita, H. Tajima, S. Futatsumori, A. Kohmura, N. Yonemoto, Y. Sumiya, An experimental evaluation on handover performance of AeroMACS prototype, in: 2016 Integrated Communications Navigation and Surveillance (ICNS), 2016. [16] K. Morioka, J. Naganawa, N. Kanada, S. Futatsumori, A. Kohmura, N. Yonemoto, Y. Sumiya, 5GHz ground-to-air communication link by AeroMACS in high-speed movement scenarios, in: 2017 23rd Asia-Pacific Conference on Communications (APCC), 2017. [17] J. Naganawa, K. Morioka, J. Honda, N. Kanada, N. Yonemoto. Y. Sumiya, Antenna configuration mitigating ground reflection fading on airport surface for AeroMACS, in: IEEE Conference on Antenna Measurements & Applications (CAMA), 2017. [18] K. Morioka, X. Lu, N. Kanada, S. Futatsumori, N. Yonemoto, Y. Sumiya, A. Kohmura, Field taxing experiments of aircraft access to SWIM over AeroMACS, in: 2018 IEEE Conference on Antenna Measurements & Applications (CAMA), 2018. [19] K. Morioka, J. Naganawa, X. Lu, N. Miyazaki, A. Kohmura, N. Yonemoto, Y. Sumiya, QoS validation tests for aircraft access to SWIM by ground taxiing experiments, in: Proceedings of the International Symposium on Autonomous Decentralized Systems (ISADS), 2019. [20] I.A. Moschakis, H.D. Karatza, A meta-heuristic optimization approach to the scheduling of bag-of-tasks applications on heterogeneous clouds with multi-level arrivals and critical jobs. Simulation Modelling Practice and Theory 57 (2015) 1–25. [21] G.L. Stavrinides, F.R. Duro, H.D. Karatza, F.J.G. Blas, J. Carretero, Different aspects of
workflow scheduling in large-scale distributed systems. Simulation Modelling Practice and Theory 70 (2017) 120–134. [22] G.L. Stavrinides, H.D. Karatza, The impact of workload variability on the energy efficiency of large-scale heterogeneous distributed systems. Simulation Modelling Practice and Theory 89 (2018) 135–143. [23] C. Cicconetti, L. Lenzini, E. Mingozzi, Quality of service support in IEEE 802.16 networks, IEEE Network 20 (2006) 50–55. [24] C. Cicconetti, A. Erta, L. Lenzini, E. Mingozzi, Performance evaluation of the IEEE 802.16 MAC for QoS support, IEEE Trans. Mobile Comput. 6(1) (2007) 26–38. [25] K. Wongthavarawatn, A. Ganzz, Packet scheduling for QoS support in IEEE 802.16 broadband wireless access systems, Int. J. Commun. Syst. 16(1) (2003) 81–96. [26] W. Nie, H. Wang, J.H. Park, Packet scheduling with QoS and fairness for downlink traffic in WiMAX networks, J. Inf. Process. Syst. 7(2) (2011) 261–270. [27] J. Chen, W. Jiao, Q. Guo, An integrated QoS control architecture for IEEE 802.16 broadband wireless access systems, in: IEEE Global Telecommunications Conference, 2005. [28] J. Chen, W. Jiao, H. Wang, A service flow management strategy for IEEE 802.16 broadband wireless access systems in TDD mode, in: IEEE International Conference on Communications vol. 5, 2005, pp. 3422–3426. [29] M. Katevenis, S. Sidiropoulos, C. Courcoubetis, Weighted round-robin cell multiplexing in a general-purpose ATM switch chip, IEEE J. Selected Areas Comm. 9(8) (1991) 1265–1279. [30] M. Shreedhar, G. Varghese, Efficient fair queueing using deficit round robin, IEEE Trans. Networking 4(3) (1996) 375–385. [31] L. Georgiadis, R. Gu´erin, A. Parekh, Optimal multiplexing on a single link: Delay and buffer requirements, IEEE Trans. Inf. Theory 43(5) (1997) 1518–1535. [32] B. Haowei, A. Mohammed, I. William, QoS support in ARINC 664 P8 data networks: ATN applications over TCP/IP ground-to-ground subnetworks, Journal of Aerospace Computing, Information, and Communication 3 (2006) 374–387. [33] I. Antonio, M. Antonella, M. Salvatore, IP with QoS guarantees via Geo satellite channels: performance issues, IEEE Personal Communications 8 (2001) 14–19. [34] M. Antonella, D.R. Floriano, M. Salvatore, T. Mauro, A scalable framework for in IP-oriented terrestrial-GEO satellite networks, IEEE Communications Magazine 43 (2005) 130–137. [35] L. Jie, G. Erling, S. Zhiqiang, L. Long, X. Hongwei, An interference-based distributed TDMA scheduling algorithm for aeronautical ad hoc networks, in: IEEE International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (2013) 453–460. [36] L. Qin, W. Junfeng, Multiple QoS parameters-based routing for civil aeronautical ad hoc networks, IEEE Internet of Things Journal 4 (2017) 804–814.
[37] B. Giulio, F. Romano, M. Dania, M. Luigia, F. Mario, An efficient subcarrier allocation method for AeroMACS-based communication systems, IEEE Transactions on Aerospace and Electronic Systems 49 (2013) 786–797. [38] https://www.nsnam.org/ [39] J. Farooq, T. Turletti, An IEEE 802.16 WiMAX module for the NS-3 simulator, in: Proceedings of the 2nd International Conference on Simulation Tools and Techniques, 2009. [40] M.A. Ismail, G. Piro, L.A. Grieco, T. Turletti, An improved IEEE 802.16 WiMAX module for the ns-3 simulator, in: Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques, 2010. [41] https://www.nsnam.org/wiki/NetAnim [42] https://www.wireshark.org/
Service assurance packet-scheduling algorithm for a future aeronautical mobile communication system Kazuyuki Morioka , Xiaodong Lu , Junichi Naganawa , Norihiko Miyazaki , Naruto Yonemoto , Yasuto Sumiya , Akiko Kohmura PII: DOI: Reference:
S1569-190X(19)30190-X https://doi.org/10.1016/j.simpat.2019.102059 SIMPAT 102059
To appear in:
Simulation Modelling Practice and Theory
Please cite this article as: Kazuyuki Morioka , Xiaodong Lu , Junichi Naganawa , Norihiko Miyazaki , Naruto Yonemoto , Yasuto Sumiya , Akiko Kohmura , Service assurance packet-scheduling algorithm for a future aeronautical mobile communication system, Simulation Modelling Practice and Theory (2019), doi: https://doi.org/10.1016/j.simpat.2019.102059
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
Service assurance packet-scheduling algorithm for a future aeronautical mobile communication system Kazuyuki Morioka*, Xiaodong Lu, Junichi Naganawa, Norihiko Miyazaki, Naruto Yonemoto, Yasuto Sumiya, and Akiko Kohmura National Institute of Maritime, Port and Aviation Technology, Electronic Navigation Research Institute (MPAT/ENRI) 7-42-23, Jindaiji-higashimachi, Chofu, Tokyo 182-0012, Japan Abstract In recent years, realizing safe and efficient air traffic management (ATM) has become one of the most important social issues because of the rapid increase in air traffic volume. To realize safe and efficient ATM, it is necessary for related stakeholders to share aeronautical big data by connecting to aircrafts and ground systems efficiently and globally. However, the current aeronautical communication systems are not adapted to be used in support of global collaborative decision making (CDM) through information exchange. In order to accelerate the implementation of information collaborative environment and improve operational awareness in the aeronautical domain, this study focuses on an information platform for sharing aeronautical big data, and a communication medium for achieving Internet of aeronautical things. This paper proposes a new packet-scheduling algorithm for a next-generation aeronautical mobile communication system that includes various types of services and devices. The algorithm balances service assurance and service fairness using strict priorities for real-time services and loose priorities for non-real-time services. Performance evaluations were conducted using a network simulator to confirm the effectiveness of the proposed algorithm. First, a simple single-device scenario was utilized to confirm the effectiveness of the quality of service function for an aeronautical mobile communication system. Second, a multiple-device scenario was employed to evaluate the performance of the proposed scheduler. Finally, an airport scenario was used to demonstrate the effectiveness of our algorithm in a more practical situation. Furthermore, we show that the airport scenario is useful for evaluating the overall performance of a new aeronautical mobile communication system in the system introduction phase or cell design phase.
Keywords: Aeronautical big data, System Wide Information Management (SWIM), Aeronautical IoT, Aeronautical Mobile Airport Communications System (AeroMACS), orthogonal frequency-division multiple access (OFDMA), quality of service (QoS), packet-scheduling algorithm ------------------------------------------------*
Corresponding author
Email address: [email protected] 1. Introduction In recent years, realizing safe and efficient air traffic management (ATM) has become one of the most important social issues because of the rapid increase in air traffic volume. To realize safe and efficient ATM, it is necessary for aeronautical stakeholders, such as pilots, air traffic controllers, airline operators, and airport operators, to share big data related to aircraft operations efficiently and globally. (A’) Nowadays, big data analytics, cloud computing, and IoT have been receiving considerable attention to create new services in several industrial sectors. In the aviation sector, there has also been a growing interest in AI and big data analytics for decision making based on aeronautical information shared by IoT and the cloud. For example, critical decision making and predictive analytics for trajectory management are studied in [1]. Furthermore, mobile edge computing for air-to-ground integration systems, which are advanced versions of aeronautical IoT, is discussed in [2]. However, the current ATM information systems face difficulties in terms of system integration because they are based on different data formats and exchange protocols. To solve this issue, the International Civil Aviation Organization (ICAO) has standardized a future aeronautical information-sharing platform, called System Wide Information Management (SWIM) [3]. Using SWIM, efficient system integration can be realized by sharing aeronautical big data, such as flight, weather, and surveillance data using the standardized data formats and exchange protocols. To realize SWIM, communication between moving aircraft and fixed ground systems is necessary, and an effective aeronautical mobile communication system is important for this purpose. However, the current aeronautical mobile communication system, i.e., VHF datalink mode 2, was developed decades ago and has a maximum transmission rate of only 31.5 kbps. In addition, it is not compatible with the common internet protocol (IP) and has security problems because it is not encrypted. Therefore, it is difficult for current aeronautical mobile communication system to realize SWIM. To solve these issues, the ICAO has also standardized a future aeronautical communication system, called the Aeronautical Mobile Communication System (AeroMACS) [4–6]. AeroMACS is based on the IEEE802.16 standard [7] for use on the airport surface and enables rich information sharing via image or video transmission through a broadband wireless channel. In addition, the AeroMACS is compatible with IP, thereby reducing the system introduction and application development costs. Furthermore, it is more secure than the conventional aeronautical mobile communication system, because it is encrypted in the multiple protocol layers. These advantages make it promising medium to realize aeronautical IoT. Since the IEEE802.16 standard is targeted at public broadband wireless access, it offers best
effort (BE) services; consequently, the more users are in the serving cell, the worse the service quality becomes. On the other hand, AeroMACS was developed for air traffic control services (ATS), which necessitate high, stable quality due to the stringent aircraft safety requirements. Fig. 1 illustrates the usage of AeroMACS around an airport. Aeronautic communication can be classified as ATS communication or airline operational control (AOC) communication based on its content. In particular, ATS communication, which includes air traffic control messages in the form of analog/digital voice communication between pilots and air traffic controllers, is the most important
Fig. 1. Multi-media services using AeroMACS at an airport. type of communication for aircraft safety. In addition, controller–pilot data link communication (CPDLC), which is a typical data exchange service for air traffic control, is categorized as ATS communication. Meanwhile, AOC communication includes flight plan data exchanged among the staff of each airline to support their aircraft operations. Other than the data falling into the ATS and AOS categories, there are aircraft surveillance data obtained by aeronautical radar; automatic terminal information service (ATIS) data, which include information about the weather around the airport and runway status information; and so on. Thus, aeronautical communication systems must handle different types of information with various importance levels. In addition to the different types of information content, there are various types of user devices in aeronautical mobile communication systems. Aircraft are the largest of these user devices, as well
as having the highest speeds among these devices and difficulty turning in small radii; therefore, assistance from outside the aircraft via a wireless channel is necessary. The vehicles driving in the airport constitute the second most important type of user devices. These vehicles include aircraft-towing cars, cargo carriers, security cars, refueling cars, and airline buses that carry passengers or crews to/from the aircraft. The drivers can recognize the surroundings of their vehicles by themselves, so the importance of communication on these vehicles is lower than that on aircraft. The importance of the devices carried by airport workers and fixed sensor devices such as weather sensors and security video cameras is lower than that of the aircraft and vehicles. In this research, we consider SWIM as a platform for aeronautical big data, and AeroMACS as a medium for aeronautical IoT. This concept is referred to as aircraft access to SWIM (AAtS) over AeroMACS and was firstly proposed in [8,9]. We developed prototype SWIM [10–12] and AeroMACS [13–17] systems, then conducted field trials of AAtS [18]. (C’) In [19], we presented the results of practical testing for AAtS on the airport and demonstrated the effectiveness of quality of service (QoS) function in AeroMACS. The scenario used only one user’s device for proof of the concept. In the practical application, several devices will access the system and use various types of services simultaneously under different conditions. In this situation, network simulation is considerably more effective than practical test in terms of cost and resource. In this paper, we consider the cases which include several devices and various services simultaneously. Moreover, a scheduling algorithm for AeroMACS is proposed and evaluated in this paper. (B’) Furthermore, when numerous devices access the system and use various services simultaneously, the QoS function plays an important role. To assure total service quality, the QoS control has to be considered in multiple layers adequately. QoS control in the application layer is crucial, as it is the closest part to the end user. A task scheduling method in heterogeneous clouds was studied in [20]. The authors stated that traditional single layered scheduling approaches do not work and multi-layered approach should be considered in a heterogeneous environment. They proposed two multi-criteria meta-heuristic algorithms for scheduling optimization of bag-of-tasks (BoT) applications on an interlinked cloud system. The idea that uses the multi-layered approach in the heterogeneous environment is reasonable, and we also consider the multi-layered scheduling algorithm for radio resource management problems in this paper. In [21], the workflow scheduling problem from the QoS and data locality perspectives in large-scale distributed systems were investigated. They proposed a scheduling approach, considering synchronization models for the tasks in a work-flow application. Furthermore, the impact of workload variability was investigated in terms of computational volume and interarrival times in [22]. They employed four heuristics for the scheduling of the workload to evaluate the system performance in terms of QoS. These studies considered QoS in the application layer.
In addition to QoS control in the application layer, QoS control in the communication layer is also important to assure the total QoS that is proposed and discussed in this paper. In particular, priority handling for the radio channel, which is the most congested in the end-to-end communication pass, is essential in SWIM access that involves various types of services and devices. Therefore, a study of the QoS function for AeroMACS is very important for SWIM access. There are several papers related to the QoS function for IEEE 802.16 [23–28]. However, these studies were mainly focused on public communication; therefore, all devices were treated as equal and inter-device QoS was not considered. This paper proposes a new packet-scheduling algorithm to ensure that QoS requirements are met by future aeronautical mobile communication systems that include various types of services and devices. Several QoS requirements are described in the AeroMACS technical manual [5]; however, the implementation method is left for the vendors to determine. Therefore, the packet-scheduling algorithm, which is a concrete implementation method of the QoS function is a very important practical issue. In addition, the packet scheduling problem, i.e., the radio resource allocation problem in orthogonal frequency-division multiple access (OFDMA), which is a multiple access method for AeroMACS, is a kind of packing problem in the area of discrete mathematics. Thus, the packet-scheduling algorithm is an important problem not only practically, but also academically. The rest of this paper is organized as follows. Section 2 introduces the system model, the problem formulation, and several packet-scheduling algorithms for IEEE802.16. Section 3 proposes the new packet-scheduling algorithm for AeroMACS, and Section 4 describes the network simulations performed to evaluate the effectiveness of the proposed algorithm and their results. Finally, Section 5 concludes this paper.
2. System model As AeroMACS is based on the IEEE802.16 standard [7], the IEEE802.16 system model is described here. The standard is a connection-oriented system, so the subscriber station (SS) sets up a connection to the base station (BS) before the services are started. The BS allocates a connection ID (CID) to each connection; then, each connection is associated with a service flow (SF).
Fig. 2. (a) Packet scheduler in BS and (b) radio frame structure of the IEEE802.16 standard. The downlink traffic from the application layer is classified by CID, then transferred to the appropriate SF queue according to its service policy. Next, the packet scheduler in the BS decides which packet should be transmitted to the wireless channel first to meet the QoS requirements. Fig. 2(a) shows the packet scheduler in the BS. Fig. 2(b) shows the radio frame structure of the IEEE802.16 standard. The vertical axis represents the frequency domain, called subcarriers in orthogonal frequency-division multiplexing (OFDM), and the horizontal axis indicates the time domain, called symbols in OFDM. As shown in the figure, the OFDMA frame can be seen as a rectangular logical structure consisting of K subcarriers and S symbols. The BS allocates to each SS a sub-region, called a burst, which consists of k consecutive subcarriers and s consecutive symbols. Then, the packet scheduler packs the packets from the upper layer into the burst. The packet scheduler is implemented in the medium access control layer in the BS. The problem of allocating appropriate bursts to each SS to maximize the total throughput, while meeting the QoS requirements, is called a packet scheduling problem.
2.1. QoS definitions in IEEE802.16 standard The QoS definitions described in the IEEE802.16 standard are summarized in this section. The following service types are defined in the IEEE802.16 standard and are followed by AeroMACS:
Unsolicited grant service (UGS): It supports constant bitrate (CBR) or fixed-throughput connections such as voice over IP
(VoIP). This service guarantees that the throughput, latency, and jitter will be of the necessary levels.
Extended real-time polling service (ertPS): It supports CBR or fixed-throughput connections, like UGS. The difference from UGS is that
UGS allocates resources even in silence or no-packet periods, while ertPS does not allocate resources in such periods. This class is an intermediate class between UGS and real-time polling service (rtPS). It improves the overhead of UGS and the delay of rtPS simultaneously.
rtPS: It supports real-time variable bit rate (VBR) traffic such as video streaming services via
minimum reserved and maximum sustained traffic rates and requires tolerably stringent latency constraints.
Non-real-time polling service (nrtPS): It supports delay-tolerant streams with variably sized packets, for which only minimum
reserved and maximum sustained traffic rates are required, such as FTP. These applications are time-insensitive and require minimum bandwidth allocation.
BE service: It supports services such as HTTP and email. BE services are handled on a space-available
basis and do not require stringent latency/jitter constraints.
2.2. Problem statement The packet scheduling problem to maximize the total throughput while meeting the QoS requirements for each service type described in Section 2.1 can be defined as the following constrained optimization problem: 𝐿
𝐾
𝑆
1 arg max ∑ ∑ ∑ 𝑟𝑖 ,𝑘, 𝑠-𝜌𝑖 ,𝑘, 𝑠- , 𝜌 𝑇
(1)
𝑖=1 𝑘=1 𝑠=1
subject to 𝐾
𝑆
1 ∑ ∑ 𝑟𝑖 ,𝑘, 𝑠-𝜌𝑖 ,𝑘, 𝑠- = 𝑅𝑖max , ∀𝑆F ∈ *UGS+ 𝑇
(2)
𝑑𝑖 ≤ 𝐷𝑖 , ∀SF ∈ *UGS, ertPS, rtPS+
(3)
𝑘=1 𝑠=1
𝐾
𝑅𝑖min ≤
𝑆
1 ∑ ∑ 𝑟𝑖 ,𝑘, 𝑠-𝜌𝑖 ,𝑘, 𝑠- ≤ 𝑅𝑖max , ∀𝑆F ∈ *ertPS, rtPS, nrtPS+ 𝑇
(4)
𝑘=1 𝑠=1
𝜌𝑖 ,𝑘, 𝑠- ∈ *0,1+, ∀𝑖, 𝑘, 𝑠
(5)
𝐿
∑ 𝜌𝑖 ,𝑘, 𝑠- = 1, ∀𝑘, 𝑠,
(6)
𝑖=1
where i, k, and s are the indices representing the connection, subcarrier, and symbol, respectively, and L, K, and S are the total numbers of connections, subcarriers, and symbols, respectively. 𝑟𝑖 ,𝑘, 𝑠shows the maximum number of bits per symbol, which can be transmitted by the i-th connection using the k-th subcarrier and s-th symbol. 𝜌𝑖 ,𝑘, 𝑠- is the allocation index of the i-th connection for the k-th subcarrier and s-th symbol. Hence, if 𝜌𝑖 ,𝑘, 𝑠- = 1, then the corresponding resource is allocated to the i-th connection. Eqs. (5) and (6) indicate that one resource can be allocated to only one connection. 𝐷𝑖 is the allowable maximum delay for a real-time service. 𝑅𝑖min and 𝑅𝑖max are the minimum reserved traffic rate and maximum sustained traffic rate, respectively. Finally, T represents the duration of an OFDM frame. Obtaining the optimal solution of Eqs. (1)–(6) is difficult, because there are many parameters to consider. Therefore, several algorithms that are sub-optimal but easy to implement were proposed in [23–28]. This two-dimensional rectangular mapping problem is a kind of the bin packing problem known as an NP-hard problem. Thus, the problem considered in this paper is important not only practically, but also academically.
2.3. Scheduling algorithm for IEEE802.16 standard In the previous section, we described the packet scheduling problem considered in this paper. Next, we review some of the scheduling algorithms that have been proposed so far. The simplest one is the round robin (RR) scheduler. The RR scheduler allocates radio resources to SSs in order equally, without considering their priority. This scheduler is simple and easy to implement; however, it is inappropriate for systems that have different levels of priorities and those in which the packet size varies frequently. The weighted RR (WRR) scheduler [29], which is an extended version of the RR scheduler, allocates a kind of weight to each SF. Then, the scheduler distributes resources based on the weight, which is adjusted according to the QoS requirements. The deficit RR (DRR) scheduler [30] associates a deficit counter to each SF. Then, the scheduler distributes resources based on the deficit counter, which is adjusted according to the QoS requirements. The WRR and DRR schedulers belong to the same category. The WRR and DRR schedulers were previously applied as uplink and downlink packet schedulers, respectively, for the IEEE802.16 standard [23,24]. These algorithms are focused on throughput assurance; therefore, they are appropriate for non-real-time services. For real-time services, the earliest deadline first (EDF) [31] scheduler, which allocates resources to the SS that has the first deadline, is adequate. The algorithms mentioned above target single service and do not consider the priorities of
multiple services. To ensure sufficient QoS among multiple services that have different levels of importance, hierarchical scheduling algorithms were proposed in [25–28]. Kitti and Aura [25] and Niell [26] applied two layered-hierarchical schedulers separately to the uplink and downlink for the IEEE802.16 standard. The incoming packets are allocated to a priority queue according to the service class in the first layers of these algorithms. Here, strict priorities are applied; hence, the most important priority queue is served first, and the second most important priority queue is served when the first queue becomes empty. UGS has the highest priority, followed sequentially by ertPS, rtPS, nrtPS, and BE services. In the second layers of these algorithms, the different schedulers are applied to each SFs to meet the QoS requirements. Fixed allocation is applied for UGS, the WRR scheduler is adapted for ertPS and rtPS, and the RR scheduler is used for BE services. The drawback of these algorithms is that the low priority queue is not served when the high priority queue is always busy. To solve this problem, deficit fair priority queue (DFPQ)-based second layer hierarchical scheduling algorithms were proposed in [27,28]. The DFPQ loosens the inter-service priority order in the first layer using a deficit counter. The deficit counter is decreased according to the allocated packet size. The next most important queue is served when the counter becomes zero. In the second layer, different schedulers are applied to each SF to meet the QoS requirements, as in [25,26]. The DFPQ-based algorithms are superior in terms of fairness for public broadband wireless access.
(D’) 2.4. QoS control for aeronautical telecommunication network Aeronautical telecommunication network (ATN) has been developed by ICAO to integrate air-ground and ground-ground data communications for air traffic control and aeronautical operational communications. QoS control plays an important role in ATN, to carry time critical information required for aircraft safety. In ATN, giving priority has the essential role to ensure that high priority safety and time critical data are not delayed by low priority non-safety data [32]. QoS control and the scheduling algorithm for satellite communication system are discussed in [33, 34]. Furthermore, QoS control for aeronautical ad hoc network (AANET) is studied in [35, 36]. A radio frame scheduling algorithm for time division multiple access was proposed in [35]. A multiple QoS parameters-based routing protocol (MQSPR) was studied in [36], to improve the overall network performance for communication between the aircraft and the ground. This paper considers AeroMACS. As noted in [6], scheduling and resource management algorithms are not specified in the AeroMACS standard, and hence, thus far, they have been open issues requiring additional research efforts. A resource allocation algorithm for AeroMACS was discussed for increasing the communication system capacity using the tap correlation related to each
user communication channel, which is a typical feature of an airport propagation model [37]. The algorithm aimed for maximizing communication rate utilizing channel information. However, it did not consider QoS parameters defined in AeroMACS standard [5]. Our proposed algorithm balances service assurance and service fairness considering QoS parameters defined in [5], while adapting strict priorities for real-time services and fair priorities for non-real-time services.
3. Proposed method Some packet-scheduling algorithms for the IEEE802.16 standard were summarized in the previous section. As the hierarchical scheduling algorithm is superior for ensuring that the QoS requirements are met for various SFs, we also adopted a hierarchical structure in our scheduling algorithm for AeroMACS. The difference between IEEE802.16 and AeroMACS is that IEEE802.16 is targeted at public access in which users are treated equally, while AeroMACS is targeted at aeronautical use, which has various types of user devices: aircraft, vehicles, sensors, and so on. In this section, additional requirements for AeroMACS are described firstly; then, a new packet-scheduling algorithm for AeroMACS is proposed.
3.1. Device class The SSs in AeroMACS are equipped in various devices such as aircraft, vehicles, and sensors. Therefore, new QoS classes are defined in the AeroMACS technical manual as follows:
Aircraft – the modems installed in aircraft. Considering both safety and non-safety applications, all identified types of SFs are recommended for aircraft.
Vehicle – devices hosted on all ground support vehicles, including passenger vans/buses, dollies for carrying cargo, refueling trucks, catering vehicles, push-back tugs/tractors, etc.
Ground critical – fixed devices used to monitor/control ground equipment that are deployed for critical ATS services, such as radar, landing systems, runway lighting controls, etc.
Ground default – all other ground fixed or nomadic equipment supporting the safety and regularity of flight operations.
Table 1 shows the QoS parameters described in the AeroMACS technical manual. VoIP in this table represents digital VoIP and indicates that UGS or ertPS should be used for VoIP. NET shows the management data for an ATM network (ATN), and ATS and AOC represent communications for air traffic control services and airline operational services, respectively. This table demonstrates that
rtPS should be used for NET, ATS1, and ATS2, while nrtPS should be used for ATS3 and AOC1. DEFAULT shows that services other than ATS/AOC, such as sensor data, and BE services should be used for the DEFAULT service classes. MSTR represents the maximum sustained traffic rate, which defines the maximum data rate that can be allocated for the corresponding connection. MRTR is the minimum reserved traffic rate, which defines the minimum data rate that must be allocated for the corresponding connection. Finally, ML indicates the maximum latency, which is the maximum allowable delay.
Table 1 QoS parameter values in the AeroMACS downlink SFs [5]. Service
Service
Class
Type
QoS Parameters
Mandatory Services for Each Device Type
MSTR
MRTR
ML
(kbps)
(kbps)
(s)
Aircraft
Vehicle
Ground
Ground
Critical
Default
X
VoIP1
UGS (ertPS)
64
64
0.15
X
X
NET
rtPS
34
32
1
X
X
X
ATS1
rtPS
34
32
1.5
X
X
X
ATS2
rtPS
34
32
1.5
X
ATS3
nrtPS
34
32
N/A
X
AOC1
nrtPS
67
64
N/A
X
DEFAULT
BE
N/A
N/A
N/A
X
X
X
X
Although the QoS requirements are described in detail in Table 1, the concrete means of implementation, such as the packet-scheduling algorithm, are left for the vendor to decide. Therefore, we propose a new packet-scheduling algorithm for AeroMACS in the next section.
3.2. Scheduling algorithm for AeroMACS In this section, we propose a new packet-scheduling algorithm for AeroMACS to meet the requirements described in Section 3.1. Fig. 3 provides an overview of the proposed algorithm, which consists of three-layered hierarchical schedulers. This algorithm balances service assurance and fairness using strict priorities for the real-time services and loose priorities for the non-real-time services.
Fig. 3. Overview of the proposed algorithm.
Fig. 4. Weight model for the proposed algorithm. In the first layer, our algorithm schedules the service types based on strict priorities, because communication delays in the ATM may impair the safety of flight operation. UGS, ertPS, and rtPS, which have stringent delay requirements, are scheduled first in that order. Then, nrtPS and BE, which are non-real-time services, are scheduled in that order.
In the second layer, our algorithm schedules the device class. As noted in Section 3.1, the device classes newly defined in the AeroMACS technical manual are aircraft, vehicle, ground critical, and ground default. The aircraft are the largest devices, have the highest speeds, and have difficulty turning in small radii; therefore, assistance from outside the aircraft via a wireless channel is necessary. Thus, the aircraft should have the highest priority. In our scheduler, a strict priority queue based on device class is used for real-time services such as UGS, ertPS, and rtPS, which have stringent delay requirements. The aircraft have the highest priority, followed sequentially by vehicles, ground critical devices, and ground default devices. This order ensures high QoS for the real-time services of the aircraft. On the other hand, a fair scheduler is used for the non-real-time services such as nrtPS and BE, to prevent the high priority services from occupying the resources. Finally, in the third layer, our algorithm uses fixed allocation scheduler for the UGS and ertPS, the EDF scheduler for the rtPS, the WRR scheduler for the nrtPS, and the RR scheduler for BE. (G’) Fig. 4. illustrates the weight model in real-time/non real-time and critical/non critical axes. The numbers in the figure represent the weight-level for each service. The smaller numbers indicate high weight, and the larger numbers indicate low weight. Fig. 5 shows the proposed algorithm for AeroMACS. Proposed algorithm is an extension of a two-layer scheduling algorithm to a three-layer one and can be seen as a combination of strict priorities [25,26] and loose priorities [27,28]. The third layer exists for at most three services: UGS, ertPS, and rtPS. In addition, there are only four device classes. Therefore, the increase in computational complexity is limited.
4. Performance evaluation A new packet-scheduling algorithm for the AeroMACS was proposed in the previous section. Here, we describe the evaluation of the effectiveness of the algorithm, which was performed using the ns3, an event-driven network simulator that is widely used in academic research [38]. The ns3 simulator includes the WiMAX module [39,40], and we implemented our packet scheduler using this module. We also implemented an automatic modulation and coding (AMC) function, which adaptively selects the appropriate modulation and coding scheme according to the channel status. Firstly, a simple single-device scenario was utilized to confirm the effectiveness of the QoS function in the aeronautical mobile communication system. Secondly, a multiple-device scenario was employed to evaluate the performance of the proposed scheduler. Finally, an airport scenario was used to show the effectiveness of our method in a more practical situation. Furthermore, we demonstrated that the airport scenario is useful for evaluating the overall system performance in the system introduction or cell design phase.
4.1. Simulation setup The simulation setup is described in this section. In our simulation, the received signal strength
fluctuated according to the movement of the SSs. Then, the AMC was adopted to follow the channel fluctuations automatically. The propagation model was the free space loss model, which is the simplest model. Furthermore, symbol base allocation was used to assign a radio frame. Hence, the number of sub-carriers was fixed to K, and the two-dimensional allocation problem was reduced to a one-dimensional allocation problem. This restriction did not affect the evaluation results for the packet-scheduling algorithm.
Fig. 5. Proposed scheduling algorithm for AeroMACS.
Table 2 Simulation parameters. Traffic Pattern for Aircraft SSs Service
Service Type
Size (bytes)
Interval (ms)
Throughput (kbps)
VoIP1
UGS
160
20
64
NET
rtPS
400
100
32
ATS1
rtPS
400
100
32
ATS2
rtPS
400
100
32
ATS3
nrtPS
400
100
32
AOC1
nrtPS
800
100
64
DEFAULT
BE
800
100
64
Class
Traffic Pattern for Vehicle SSs Service
Scheduling Type
Size (bytes)
Interval (ms)
Throughput (kbps)
VoIP1
UGS
160
20
64
NET
rtPS
400
100
32
ATS1
rtPS
400
100
32
DEFAULT
BE
800
100
64
Class
Traffic Pattern for Ground Default SSs Service
Scheduling Type
Size (bytes)
Interval (ms)
Throughput (kbps)
NET
rtPS
400
100
32
DEFAULT
BE
800
100
64
Class
Table 2 shows the traffic patterns for the SSs used in the simulation, which were determined based on the QoS requirements shown in Table 1. Each aircraft device used seven services. VoIP1 assumed voice service between the pilot and air traffic controller and sent 160-byte packets in 20 ms intervals. NET assumed management packets in an ATN, and ATS1, ATS2, and ATS3 assumed data services related to ATC, such as CPDLC. NET, ATS1, ATS2, and ATS3 sent 400-byte packets in 100 ms intervals. AOC1 assumed data communication for airline operation. DEFAULT assumed data communication other than ATS/AOC. AOC1 and DEFAULT sent 800-byte packets in 100 ms intervals. The total throughput per aircraft was 320 kbps. The vehicle devices used four services: VoIP, NET, ATS1, and DEFAULT. The total throughput per vehicle was 192 kbps. The ground default devices used NET and DEFULT. The total throughput per ground default device was 96 kbps. Ground critical devices were not included in our scenarios for simplicity.
4.2. Single-device-class scenario Using the setup described in the previous section, we conducted simulations for various scenarios. Firstly, a simple single-device-class scenario was utilized to confirm the effectiveness of the QoS function in the aeronautical mobile communication system. In this scenario, we assumed that there were aircraft devices only and compared the performance between the QoS-enabled and QoS-disabled cases while increasing the number of aircraft devices. Here, we assumed that the aircraft did not move and that the signal-to-noise ratio (SNR) was constant. Therefore, the modulation format did not change. It is set to 64QAM with 3/4 rate convolutional coding. Fig. 6 compares the throughput between the QoS-enabled case (a) and QoS-disabled case (b). The throughput here is the total number for the same service type. Firstly, we compare UGS (red line in the figure), which is the most urgent service type, between the two cases. In the QoS-enabled case, the UGS throughput reaches the limit when the number of aircraft devices becomes 20. On the other hand, the UGS throughput increases as the number of the aircraft devices increases in the QoS-disabled case.
Fig. 6. Throughput comparison for each service type. Next, we compared nrtPS and BE services, which are non-real-time services. In the QoS-enabled case, the throughputs decrease when the number of aircraft devices exceeds 20. On the other hand, the throughputs of nrtPS and BE services are constant when the number of aircraft devices exceeds 20 in the QoS-disabled case. These results demonstrate that the UGS, which has higher priority, was prioritized over nrtPS and BE services, which have lower priorities, when the QoS function was enabled, confirming the effectiveness of the QoS function. Fig. 7(a) shows the total throughput of the system. The reason that the throughput reaches the limit of about 6 Mbps is that the channel bandwidth of the AeroMACS per channel is 5 MHz;
therefore, the maximum throughput becomes about 7.5 Mbps. In addition, some protocol overhead is included, such as protocol headers and control packets. Then, the maximum throughput of the system becomes about 6 Mbps per channel.
Fig. 7. Comparison of total throughput and total packet loss rate between the QoS-enabled and QoS-disabled cases.
From Fig. 7(a), we can see that the total throughput in the QoS-enabled case becomes lower than that in the QoS-disabled case when the number of aircraft devices exceeds 20. The reason for this behavior is that the number of UGS packets, which were small, increased as the prioritization and then the relative overhead increased. Fig. 7(b) shows the total packet loss rate of the system. From this figure, we can see that the packet loss rate in the QoS-enabled case is lower than that in the QoS-disabled case. The reason for this difference is that the packet loss rate of the UGS improved dramatically. Note that the transmission interval of the UGS is the shortest, at 20 ms, and the number of packets transmitted per unit time becomes the largest. Therefore, the impact of the UGS on the total packet loss rate becomes large. The results in Fig. 7(b) also show the effectiveness of the QoS function in the aeronautical mobile communication system.
4.3. Multiple-device-class scenario A new packet-scheduling algorithm was proposed in Section 3.2. To confirm the effectiveness of this algorithm, a multiple-device-class scenario was employed, as described in this section. We compared our three-layered algorithm to the two-layered algorithm proposed in [25,26]. In this scenario, there were 15 aircraft devices and 20 ground default devices. Then, we evaluated the throughput and packet loss rate when the number of vehicle devices increased. The traffic pattern of
each device is described in Table 2. We assumed that the aircraft did not move and that the SNR was constant. Therefore, the modulation format did not change. We used 64QAM with 3/4 rate convolutional coding, as in the previous scenario. Figs. 8(a–c) compare the throughput for each service type. Here the throughput is the total value for each device class. The dotted and solid lines show the results obtained using the existing and proposed algorithms, respectively.
Fig. 8. Comparison of the throughput for each device class. Firstly, we focus on the UGS and rtPS of the aircraft and vehicle devices. From Fig. 8(b), we can see that the throughput of the aircraft devices obtained using the existing algorithm decreases rapidly when the number of vehicle devices exceeds 35; however, it is almost constant when the proposed algorithm is used. In contrast, Fig. 8(a) shows that the throughput of the vehicle devices obtained using the proposed algorithm is lower than that resulting from using the existing algorithm when the number of vehicle devices exceeds 35. These results demonstrate that the real-time service of the aircraft devices is prioritized over the UGS of the vehicle devices, which implies that the
proposed algorithm worked effectively. Next, we focus on the rtPS of the vehicle and ground default devices. From Fig. 8(b), we can see that the throughput of the vehicle devices obtained using the proposed algorithm is higher than that resulting from using the existing algorithm when the number of vehicle devices is between 20 and 35. On the other hand, the throughput of the ground default devices is lower than that obtained using the existing algorithm in the same range. These results demonstrate that the vehicle devices are prioritized over the ground default devices, which have lower priority than the vehicle devices, again indicating the effectiveness of our proposed algorithm. Fig. 8(d) shows the total throughput for each device class, demonstrating that the real-time service of the vehicle devices is prioritized over that of the ground default devices when the number of vehicles is between 20 and 35, while the real-time service of the aircraft devices is prioritized over that of the vehicle devices when the number of vehicle devices exceeds 35. These results confirm the effectiveness of our proposed algorithm for AeroMACS.
4.4. Practical airport scenario Finally, a practical airport scenario was utilized to confirm the effectiveness of our proposed algorithm in an airport use case. In this scenario, the SSs moved and SNR fluctuated. Therefore, the modulation and coding scheme changed adaptively. The simplest free space propagation loss model was used. The total number of aircraft devices was 15. One of them was about to take-off at a speed of 300 km/h on runway, another was taxiing at 60 km/h on taxiway, and another was waiting for take-off on the edge of the runway. The remaining 12 aircraft were parked on the apron. The total number of vehicle devices was set to 30, 40, and 50. Thirty vehicles were moving on the surrounding road at 30 km/h, and the rest were parked in random positions, but not on the runway or taxiway. The total number of ground default devices was 20, and they were fixed at random positions around the airport, but not on the runway or taxiway. The traffic model is described in Table 2 and the ground critical device is not used as the same as previous two scenarios. The length of the runway is 3 km. The transmit power of the BS is 23 dBm and the antenna gain and antenna height of the BS are 11 dB and 30 m, respectively. The antenna gain of the SS is 0 dB. The antenna heights of the aircraft device, vehicle device, and ground default device are 10 m, 2 m, and 1 m, respectively. Fig. 9 shows a screen shot of the ns3 visualization tool called Netanim [41] that was obtained while conducting the simulation described above. At this moment, a packet is being transmitted to the aircraft device parking on the apron. We can see the movement of the SS and packet flow visually.
Fig. 9. Screen shot of Netanim for a practical airport scenario. Fig. 10 shows a comparison of the jitter for the rtPS flow of an aircraft device, as the number of vehicle devices increases from 30 to 50. The jitter is defined by the average fluctuation from the average delay time. From this figure, we can observe that the jitter becomes large as the number of vehicles increases when the existing algorithm is used. This result implies that the existing algorithm does not consider inter-device priority; therefore, the real-time service of the aircraft device is affected by the service of the vehicle devices.
Fig. 10. Comparison of jitter for the rtPS flow of an aircraft device.
On the other hand, the jitter obtained using the proposed algorithm does not increase even though the number of vehicle devices increases, because the real-time service of the aircraft device is not affected by the other devices when the three-layered scheduling algorithm and inter-device priority handling are introduced. These results again confirm the effectiveness of our proposed algorithm. Finally, Fig. 11 shows the results of detailed analysis performed using Wireshark [42], which is a common packet analyzer. From this figure, we can see that the important services of the aircraft device were continued without decreasing the throughput. As shown in this example, the simulation tools and scenarios employed in this study are useful for service design, cell design, and antenna placement design in the system introduction phase.
Fig. 11. Results of detailed packet analysis performed using Wireshark [42].
5. Conclusions In this paper, we proposed a new packet-scheduling algorithm for a next-generation aeronautical mobile communication system that involves various types of services and devices. The proposed algorithm consists of three-layered hierarchical schedulers and balances the service assurance and service fairness by using strict priorities for the real-time services and loose priorities for the non-real-time services. Performance evaluations were conducted using a network simulator to confirm the effectiveness of the proposed algorithm. Firstly, a simple single-device scenario was employed to confirm the effectiveness of the QoS function in an aeronautical mobile communication system. Secondly, a multiple-device scenario was
utilized to evaluate the performance of the proposed scheduler. Finally, an airport scenario was used to demonstrate the effectiveness of our method in a more practical situation. Furthermore, we showed that the airport scenario was useful for evaluating overall system performance in the system introduction or cell design phase of a future aeronautical mobile communication system. Although only the downlink channel was considered in this study, our algorithm can be extended naturally to the uplink channel. Evaluation in an environment with co-existing downlink and uplink channels is left as future work, as is the study of multicast services that can be used for ATIS, aeronautical enroute information service (AEIS), and so on.
Acknowledgements A part of this work was supported by the Japan Civil Aviation Bureau (JCAB) for the R&D project on an advanced air-to-ground aeronautical communication system.
References [1] A. Samet, P. Johnathan, C. Paul, S. David, B. Steve, G. Gary, Predictive analytics with aviation big data, in: IEEE International Conference on Integrated Communications, Navigation and Surveillance Conference (ICNS), 2013. [2] Z. Zhenyu, F. Junhao, T. Lu, H. Yejun, G. Jie, An air-ground integration approach for mobile edge computing in IoT, IEEE Communications Magazine 56 (2018) 40–47. [3] Manual on System Wide Information Management (SWIM) Concept, ICAO Doc 10039. [4] AeroMACS Standards and Recommended Practices (SARPs), ICAO Annex 10, Vol. III, Ch. 7, 2016. [5] Manual on the Aeronautical Mobile Airport Communications System (AeroMACS), ICAO Doc 10044, 2017. [6] G. Bartoli, R. Fantacci, D. Marabissi, AeroMACS: A new perspective for mobile airport communications and services, IEEE Wireless Commun. 20 (2013) 44–50. [7] IEEE std 802.16-2009, Part 16: Air Interface for Broadband Wireless Access Systems, IEEE, 2009. [8] C. Antonio, A. Rafael, Aircraft access to SWIM (AAtS) for airport surface operations: A system design methodology, in: Proceedings of the Integrated Communication, Navigation, and Surveillance Conference (ICNS), 2016. [9] R. Apaza, B. Abraham, T. Maeda, AAtS over AeroMACS technology trials on the airport surface, in: Proceedings of the Integrated Communication, Navigation, and Surveillance Conference (ICNS), 2016.
[10] X. Lu, T. Koga, System wide information management for heterogeneous information sharing and interoperability, in: Proceedings of the IEEE 13th International Symposium on Autonomous Decentralized Systems, 2017, pp. 199–204. [11] X. Lu, K. Morioka, T. Koga, Y. Sumiya, Collaborative flight and flow information exchange to achieve seamless air traffic management operation, in: Proceedings of the IEEE 13th International Symposium on Autonomous Decentralized Systems, 2019. [12] X. Lu, K. Morioka, T. Koga, Y. Sumiya, Air-ground system wide information management to achieve safe flight operation, in: Proceedings of the IEEE International Symposium on High Assurance Systems Engineering, 2019. [13] K. Morioka, N. Kanada, S. Futatsumori, A. Kohmura, T. Tomita, N. Yonemoto, Y. Sumiya, Basic characteristic evaluation of aeromacs prototype system in Sendai airport, in: 2015 Integrated Communication, Navigation and Surveillance Conference (ICNS), 2015. [14] K. Morioka, J. Naganawa, J. Honda, N. Kanada, S. Futatsumori, A. Kohmura, T. Tomita, N. Yonemoto, Y. Sumiya, Experimental evaluation on quality of VoIP over Aero MACS, in: 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), 2016. [15] J. Naganawa, K. Morioka, N. Kanada, J. Honda, T. Tomita, H. Tajima, S. Futatsumori, A. Kohmura, N. Yonemoto, Y. Sumiya, An experimental evaluation on handover performance of AeroMACS prototype, in: 2016 Integrated Communications Navigation and Surveillance (ICNS), 2016. [16] K. Morioka, J. Naganawa, N. Kanada, S. Futatsumori, A. Kohmura, N. Yonemoto, Y. Sumiya, 5GHz ground-to-air communication link by AeroMACS in high-speed movement scenarios, in: 2017 23rd Asia-Pacific Conference on Communications (APCC), 2017. [17] J. Naganawa, K. Morioka, J. Honda, N. Kanada, N. Yonemoto. Y. Sumiya, Antenna configuration mitigating ground reflection fading on airport surface for AeroMACS, in: IEEE Conference on Antenna Measurements & Applications (CAMA), 2017. [18] K. Morioka, X. Lu, N. Kanada, S. Futatsumori, N. Yonemoto, Y. Sumiya, A. Kohmura, Field taxing experiments of aircraft access to SWIM over AeroMACS, in: 2018 IEEE Conference on Antenna Measurements & Applications (CAMA), 2018. [19] K. Morioka, J. Naganawa, X. Lu, N. Miyazaki, A. Kohmura, N. Yonemoto, Y. Sumiya, QoS validation tests for aircraft access to SWIM by ground taxiing experiments, in: Proceedings of the International Symposium on Autonomous Decentralized Systems (ISADS), 2019. [20] I.A. Moschakis, H.D. Karatza, A meta-heuristic optimization approach to the scheduling of bag-of-tasks applications on heterogeneous clouds with multi-level arrivals and critical jobs. Simulation Modelling Practice and Theory 57 (2015) 1–25. [21] G.L. Stavrinides, F.R. Duro, H.D. Karatza, F.J.G. Blas, J. Carretero, Different aspects of
workflow scheduling in large-scale distributed systems. Simulation Modelling Practice and Theory 70 (2017) 120–134. [22] G.L. Stavrinides, H.D. Karatza, The impact of workload variability on the energy efficiency of large-scale heterogeneous distributed systems. Simulation Modelling Practice and Theory 89 (2018) 135–143. [23] C. Cicconetti, L. Lenzini, E. Mingozzi, Quality of service support in IEEE 802.16 networks, IEEE Network 20 (2006) 50–55. [24] C. Cicconetti, A. Erta, L. Lenzini, E. Mingozzi, Performance evaluation of the IEEE 802.16 MAC for QoS support, IEEE Trans. Mobile Comput. 6(1) (2007) 26–38. [25] K. Wongthavarawatn, A. Ganzz, Packet scheduling for QoS support in IEEE 802.16 broadband wireless access systems, Int. J. Commun. Syst. 16(1) (2003) 81–96. [26] W. Nie, H. Wang, J.H. Park, Packet scheduling with QoS and fairness for downlink traffic in WiMAX networks, J. Inf. Process. Syst. 7(2) (2011) 261–270. [27] J. Chen, W. Jiao, Q. Guo, An integrated QoS control architecture for IEEE 802.16 broadband wireless access systems, in: IEEE Global Telecommunications Conference, 2005. [28] J. Chen, W. Jiao, H. Wang, A service flow management strategy for IEEE 802.16 broadband wireless access systems in TDD mode, in: IEEE International Conference on Communications vol. 5, 2005, pp. 3422–3426. [29] M. Katevenis, S. Sidiropoulos, C. Courcoubetis, Weighted round-robin cell multiplexing in a general-purpose ATM switch chip, IEEE J. Selected Areas Comm. 9(8) (1991) 1265–1279. [30] M. Shreedhar, G. Varghese, Efficient fair queueing using deficit round robin, IEEE Trans. Networking 4(3) (1996) 375–385. [31] L. Georgiadis, R. Gu´erin, A. Parekh, Optimal multiplexing on a single link: Delay and buffer requirements, IEEE Trans. Inf. Theory 43(5) (1997) 1518–1535. [32] B. Haowei, A. Mohammed, I. William, QoS support in ARINC 664 P8 data networks: ATN applications over TCP/IP ground-to-ground subnetworks, Journal of Aerospace Computing, Information, and Communication 3 (2006) 374–387. [33] I. Antonio, M. Antonella, M. Salvatore, IP with QoS guarantees via Geo satellite channels: performance issues, IEEE Personal Communications 8 (2001) 14–19. [34] M. Antonella, D.R. Floriano, M. Salvatore, T. Mauro, A scalable framework for in IP-oriented terrestrial-GEO satellite networks, IEEE Communications Magazine 43 (2005) 130–137. [35] L. Jie, G. Erling, S. Zhiqiang, L. Long, X. Hongwei, An interference-based distributed TDMA scheduling algorithm for aeronautical ad hoc networks, in: IEEE International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (2013) 453–460. [36] L. Qin, W. Junfeng, Multiple QoS parameters-based routing for civil aeronautical ad hoc networks, IEEE Internet of Things Journal 4 (2017) 804–814.
[37] B. Giulio, F. Romano, M. Dania, M. Luigia, F. Mario, An efficient subcarrier allocation method for AeroMACS-based communication systems, IEEE Transactions on Aerospace and Electronic Systems 49 (2013) 786–797. [38] https://www.nsnam.org/ [39] J. Farooq, T. Turletti, An IEEE 802.16 WiMAX module for the NS-3 simulator, in: Proceedings of the 2nd International Conference on Simulation Tools and Techniques, 2009. [40] M.A. Ismail, G. Piro, L.A. Grieco, T. Turletti, An improved IEEE 802.16 WiMAX module for the ns-3 simulator, in: Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques, 2010. [41] https://www.nsnam.org/wiki/NetAnim [42] https://www.wireshark.org/