Bandwidth allocation in UWB WPANs with ECMA-368 MAC

Computer Communications 32 (2009) 954–960

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Computer Communications journal homepage: www.elsevier.com/locate/comcom

Bandwidth allocation in UWB WPANs with ECMA-368 MAC Zhong Fan * Toshiba Research Europe Ltd., Telecommunications Research Laboratory, 32, Queen Square, Bristol BS1 4ND, UK

a r t i c l e

i n f o

Article history: Received 6 May 2008 Received in revised form 12 December 2008 Accepted 21 December 2008 Available online 30 December 2008 Keywords: WPAN ECMA-368 Resource reservation Traffic prediction

a b s t r a c t The new ECMA-368 MAC for UWB WPANs consists of two channel access methods: the distributed reservation protocol (DRP) and prioritized contention access (PCA). The exact method of medium access slot (MAS) allocation in DRP is not specified in the standard. The contribution of this paper is twofold. First, the paper describes a distributed resource allocation method in which a number of interference–avoidance rules are used to reserve slots for devices. Devices maintain sending and receiving tables to track activities in the neighbourhood. The proposed method is a simple, effective solution to the DRP MAS allocation problem, avoiding reservation conflicts and interference from hidden terminals. Secondly, for VBR MPEG-4 video traffic, we propose a bandwidth requirement calculation method based on traffic prediction. In the proposed scheme bandwidth is allocated based on accurate traffic predictions, therefore matching network resources to the traffic demand. Application QoS is maintained while network utilization is kept high. Furthermore, the simple, adaptive linear predictor does not incur much computation overhead. Simulation results have demonstrated the accuracy of the proposed prediction algorithm and effectiveness of the bandwidth allocation method. Ó 2009 Elsevier B.V. All rights reserved.

1. Introduction Recently the WiMedia Multi-band OFDM specification for future UWB (ultra wideband) networks has been standardized, which is known as ECMA-368 [1]. This solution enables short range communications at data rates up to 480 Mb/s with low power consumption, and operates in the 3.1–10.6 GHz UWB spectrum. Channel access in this standard is similar to that in the IEEE 802.15.3 standard [2], with a major difference being that ECMA-368 is fully distributed and in 802.15.3 there is a centralized piconet control entity. The ECMA-368 MAC consists of two channel access methods: the distributed reservation protocol (DRP) and prioritized contention access (PCA). DRP is a contention-free channel access scheme since it uses guaranteed time slot reservation to provide QoS support. On the other hand, PCA is very similar to IEEE 802.11e enhanced distributed channel access (EDCA) in that it is a prioritized contention-based access scheme for the slots outside the beacon period and DRP reservations. This paper focuses on DRP. The ECMA standard does not specify the method for channel time allocation in DRP, i.e., which time slot(s) in a superframe to choose to avoid collisions among multiple network devices and how many slots a specific application needs. Allocating channel time slots randomly without careful consideration can lead to heavy interference and collision, as well as large amount of communication overhead and delay due to the resolution of reservation * Tel.: +44 117 9069839; fax: +44 117 9060701. E-mail address: [email protected] 0140-3664/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.comcom.2008.12.024

conflicts. On the other hand, device manufacturers can take this opportunity to devise their own intelligent resource allocation method so as to differentiate their products from their competitors. This paper aims to answer the above questions. In this context, the paper proposes a distributed bandwidth reservation protocol for use in ECMA-368 DRP MAC. It enables interference (or collision) avoidance and addresses the hidden node and exposed node problems. Theoretically it can completely eliminate reservation conflict. Further, the paper proposes a traffic prediction-based method for calculating bandwidth requirement for MPEG-4 video traffic which is one of the dominant applications in future high speed wireless personal area networks (WPANs). Simulation results have demonstrated the accuracy of the proposed prediction algorithm and efficacy of the bandwidth allocation method. The rest of the paper is organized as follows: Section 2 describes our conflict-free time slot allocation scheme. Section 3 discusses in detail the bandwidth allocation method based on traffic prediction. Section 4 presents simulation results and Section 5 concludes the paper. 2. Proposed MAS allocation method The basic timing structure for frame exchange in ECMA-368 is a superframe. A superframe is composed of 256 medium access slots (MASs), where each MAS duration is 256 ls. Each superframe starts with a beacon period (BP), which extends over one or more contiguous MASs. DRP enables devices to reserve one or more MASs that the device can use to communicate with one or more

Z. Fan / Computer Communications 32 (2009) 954–960

neighbours. All devices that use the DRP for transmission or reception shall announce their reservations by including DRP information elements (IEs) in their beacons [1] (shown in Fig. 1). A reservation is the set of MASs identified by DRP IEs with the same values in the Target/Owner DevAddr, Owner, Reservation Type, and Stream Index fields. Reservation negotiation is always initiated by the device that will initiate frame transactions in the reservation, referred to as the reservation owner. The device that will receive information is referred to as the reservation target. The standard does not specify the method for MAS allocation in DRP, i.e. which time slot(s) in a superframe to choose to avoid collisions among multiple network devices. Instead, it includes a clause for the resolution of DRP reservation conflict, which is quite complicated and consists of seven rules. In this context, this paper proposes a distributed bandwidth reservation protocol for use in ECMA-368 DRP MAC. It enables interference (or collision) avoidance and addresses the hidden node and exposed node problems. DRP works based on the principle of TDMA (time division multiple access). To prevent interference in a TDMA network, a time slot t is considered free to be allocated to send data from a node x to a node y if the following conditions are met [3,4]: (1) Slot t is not scheduled for receiving or transmitting in either node x or y. (2) Slot t is not scheduled for receiving in any node z that is a one-hop neighbour of x. (3) Slot t is not scheduled for sending in any node z that is a onehop neighbour of y. It is obvious that the above rules address the problems of hidden and exposed terminals. We assume that all the devices in the network discover their neighbours via beacons. Each device maintains two tables: a sending table and a receiving table [4]. The sending table records the time slots a node and its neighbours currently use or are scheduled to use in the future for sending activities. The receiving table records the time slots a node and its neighbours currently use or are scheduled to use in the future for receiving activities. All the entries of both tables are updated every time a node hears a beacon with DRP IE. More specifically, from the Target/Owner DevAddr and Owner fields of the DRP IE in the beacon, a node records the sending or receiving device in the corresponding table. And from the zone bitmap and MAS bitmap of the DRP Allocation field, the node records the reserved MASs. In DRP there are two mechanisms used to negotiate a reservation as specified in the standard [1]: explicit and implicit. For explicit negotiation, the reservation owner and target use DRP Reservation Request and DRP Reservation Response command frames to negotiate the desired reservation. For implicit negotiation, the reservation owner and target use DRP IEs transmitted in their beacons. In the following we discuss in detail the changes

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and enhancements needed to facilitate the proposed MAS allocation for both explicit and implicit reservations. 2.1. Explicit reservation In explicit reservation, we propose the use of a three-way handshake procedure (similar to TCP) for reservation establishment (as shown in Fig. 2). The advantage of this mechanism is that the neighbours of both the owner and the target can have up-to-date MAS allocation information. Before the reservation owner sends a DRP Reservation Request, it first checks its sending and receiving tables and selects the MASs based on the three conditions described above. It then puts the selected MASs in the DRP Allocation field in the DRP IE to be sent within the DRP Reservation Request. On reception of a DRP Reservation Request, the reservation target checks its own sending and receiving tables to see if the requested MASs are indeed available. It shall then send a DRP Reservation Response command to the reservation owner. In the meantime the reservation target shall update its sending and receiving tables temporarily, taking into account the new reservation. If the reservation cannot be granted due to a conflict with its own or its neighbours’ reservations, the reservation target shall include a DRP Availability IE in the DRP Reservation Response command frame. Upon reception of the DRP Reservation Response with the reservation granted, the reservation owner updates its sending and receiving tables taking into account the new reservation. It then sends out a Confirmation command. The format of the Confirmation command frame is similar to that of a DRP Reservation Request, but with a different Frame Subtype value [1]. If the reservation target does not grant the reservation request, the owner has to check its tables as well as the DRP Availability IE received, and possibly choose another set of MASs. It is possible that different nodes have different views of the current utilization of MASs due to node mobility or stale information. During this negotiation process, the neighbouring nodes (neither the sender nor the receiver) hear the Reservation Request and Response commands and check their sending tables and receiving tables to see if there is any conflict. In the rare event of conflict, the neighbouring node in question sends out a Reservation Conflict message to the reservation owner with a DRP Availability IE [1]. The Conflict command should be sent with a higher priority, e.g., with a smaller inter-frame space. The format of the Conflict command frame is similar to that of a DRP Reservation Response, but with a different Frame Subtype value. The processing of the Conflict frame at the owner is similar to that of a Response frame as described above. All the nodes update their sending and receiving tables after the reserved MASs are confirmed, i.e., once negotiation for a reservation successfully completes, the reservation owner sends out the Confirmation. Those temporary entries during the negotiation process should be replaced or deleted. When the data transmission of a reservation is finished or a reservation is terminated by the reserva-

Fig. 1. DRP IE format, DRP control field format and DRP allocation field format [1].

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Fig. 2. Three-way handshake flowchart.

The idea proposed here is quite generic in that it can also be applied to other distributed TDMA-based reservation systems, e.g. HCF (hybrid coordination function) of 802.11e and distributed scheduling in IEEE 802.16 mesh mode. The cost, though, is the maintenance and storage (memory) of sending and receiving tables. The size of the tables can be reduced if the zone structure of MAS allocation is taken into account, e.g., the same sets of slots are allocated in every zone (cf. Fig. 1). However, the disadvantage of this zone structure is that it is less flexible and may require multiple DRP Allocation descriptors. An alternative would be to use one table to represent both sending and receiving activities with entries such as the following: 2 for sending, 1 for receiving, and 0 for no-activity. In this case each table entry needs 2 bits whereas only one bit is needed if two tables are used. The proposed protocol makes use of various command frames already defined in the standard (e.g., DRP Reservation Request and Response) and hence does not incur extra overhead. Fig. 3 shows a simple example similar to that in [4], in which node A needs a time slot to transmit to node B. Firstly, slots 1 and 2 are eliminated because slot 1 is used by A to send and slot 2 is used by B to receive (violating condition 1). Secondly, slots 4 and 5 cannot be used either, because they will cause collision at C and D (violating condition 2). Lastly, slots 6 and 7 cannot be reserved because D and E are sending on these slots (violating condition 3). So only slot 3 can be used. Given the bandwidth requirement of an application (say, video), the number of MASs reserved in a superframe can be easily obtained based on network transmission capacity, inter-frame space, and guard time interval. However, this only applies to constant bit rate (CBR) traffic (i.e., bandwidth requirement is constant). For more bursty variable bit rate (VBR) traffic, e.g., MPEG-4 encoded streams, dynamic MAS allocation schemes are needed, which will be elaborated in the next section. 3. Bandwidth allocation with traffic prediction

tion owner, the owner and target will remove the DRP IE from their beacons. In this case, all the nodes shall update their sending and receiving tables accordingly. Similarly, when hard or private DRP reservation blocks are released with UDA (Unused DRP Reservation Announcement) frames [1], the changes of MAS utilization should be noted in the tables. 2.2. Implicit reservation Implicit negotiation is carried out by transmitting DRP IE(s) in beacon frames. Similar to explicit reservation, a reservation owner first checks its sending and receiving tables and selects the MASs based on the three conditions described above. It then puts the selected MASs in the DRP Allocation field in the DRP IE. The device should continue to include the DRP IE for at least mMaxLostBeacons+1 (which is four as specified in [1]) consecutive superframes or until a response is received. On reception of a unicast DRP reservation request in a beacon, the reservation target shall check its own sending and receiving tables to see if the requested MASs are indeed available. It then includes a DRP reservation response IE in its beacon no later than the next superframe. If there is a reservation conflict, the reservation target shall include a DRP Availability IE in its beacon. As before, neighbouring nodes (neither the sender nor the receiver) hear the beacons and check their sending tables and receiving tables to see if there is any conflict. In the rare event of conflict, the neighbouring node in question sends out a conflict message to the reservation owner with a DRP Availability IE. If the reservation negotiation is successful (confirmed), all the nodes update their receiving and sending tables accordingly.

As wireless LANs and PANs have become increasingly popular, more and more real-time multimedia applications are carried in such networks. Among them, MPEG-4 encoded video streams are one of the dominant applications. In contrast to the previous video compression standards of MPEG-1 and MPEG-2, MPEG-4 is object-based [5]. This approach allows the media to be combined as overlays and video windows of different sizes, shapes, and transparency without compromising any object’s data. The MPEG-4 standard supports scalable image quality, digital rights management, and interactivity. It also provides a mechanism for incorporating new encoder/decoder image compression schemes as technology improves. Thus MPEG-4 has higher

Fig. 3. An example of time slot reservation.

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achievable compression ratios than MPEG-2 (with a bit rate as low as several hundred kbps) and its better coding tools make it more suitable for the Internet and wireless delivery of applications [6]. In MPEG-4, each video frame covers a number of arbitrarily shaped image regions, the so-called Video Object Planes (VOP) describing physical objects. There are three types of VOPs (or frames for simplicity): I (intra-coded) frames, P (predicted) frames and B (bi-directionally predicted) frames. After coding, frames can be grouped in a deterministic periodic sequence, called Group of Pictures (GOP). Each GOP consists of a combination of I, P, B frames, which have different statistical characteristics. A typical GOP pattern consisting of 12 frames is IBBPBBPBBPBB. The transmission of I frames is very important since they are used to encode and decode P frames and B frames. Real-time MPEG-4 video flows often have quite stringent quality of service requirements (e.g., delay, jitter, and bandwidth) and meeting these requirements is a challenge in wireless networks. In particular, how to dynamically allocate channel time (e.g., MAS slots) for VBR MPEG-4 video traffic in WPANs and WLANs is an open problem. Because VBR traffic can have very different frame sizes from one frame to another, fixed channel time allocation (e.g., according to fixed-size I, P, B frames) results in less optimal performance, e.g., lower channel utilization, higher delay, and higher jitter. In this paper, we propose a bandwidth allocation method based on MPEG-4 traffic prediction. In the proposed scheme MASs are allocated based on accurate traffic predictions, therefore matching network resources to the traffic demand. Application QoS is maintained while network utilization is kept high. Furthermore, the simple, adaptive linear predictor does not incur much computation overhead. Simulation results have demonstrated the accuracy of the proposed prediction algorithm and efficacy of the bandwidth allocation method.

peak-rate-based allocation often results in network under-utilization and bandwidth wastage. For example, the study on 802.11e in [13] shows that the delay from a reference scheduler experienced by a VBR MPEG-4 stream is substantially higher than that of its CBR equivalent which has the same bit rate and mean packet size as the MPEG-4 trace, but is transmitted via constant MSDUs (MAC service data units). VBR sources are highly variable in both arrival rate and packet size. An accepted stream hence either underutilizes an awarded transmission opportunity, or is allocated a transmission opportunity not sufficiently proportioned to its queue length, resulting in poor performance from the reference scheduler. In contrast, CBR sources with fixed arrival patterns and constant packet sizes are well catered for by the fixed scheduler. Therefore it is important to devise an effective and intelligent approach to support multimedia applications such as VBR video with stringent bandwidth and delay requirements. In a practical context, for example, in a home network with multiple high-bandwidth devices (e.g. HDTV, DVD), it is imperative to have an adaptive bandwidth allocation scheme in place to meet the QoS requirements of all the applications, e.g., lower delay jitter, higher throughput and higher link utilization. In this paper, we propose a simple adaptive linear prediction algorithm that a device employs to predict the bandwidth requirements for future frames. This prediction, in turn, can be used to allocate MASs according to traffic demand. Past works have shown that simple prediction techniques can predict VBR video traffic successfully (in terms of prediction error) in wired networks such as ATM and IP networks [7]. Adaptive linear prediction does not require prior knowledge of the video statistics, nor does it assume stationarity, and is thus very suitable for on-line real-time applications. Assume the rate of the nth frame of a VBR traffic stream is sðnÞ. A standard Mth-order one-step linear predictor has the form

3.1. Related work

s0 ðn þ 1Þ ¼

3.2. Traffic prediction It has been widely recognized that VBR video traffic such as MPEG-4 is highly bursty (with a high peak-to-mean ratio), highly correlated and often has heavy tail distributions [7]. An example of such traffic stream is shown in Fig. 4. If bandwidth is statically allocated according to the mean data rate of the traffic stream, large queues, large delays, and excessive packet loss can arise. In particular, if the time required to send an entire frame is longer than the allocated time slots, the remaining fragments of the frame have to be transmitted in the next superframe. This can cause excessive delays or packet drops due to missing the deadline and consequently deteriorate the video quality. On the other hand,

wðlÞsðn  lÞ ¼ W T SðnÞ;

l¼0

where M is the order of the linear predictor, and wðlÞ; l ¼ 0; . . . ; M  1, are the prediction filter coefficients, W ¼ ½wð0Þ; wð1Þ; . . . ; wðM  1ÞT , and SðnÞ ¼ ½sðnÞ; sðn  1Þ; . . . ; sðn  M þ 1ÞT . The prediction error is eðnÞ ¼ sðn þ 1Þ  s0ðn þ 1Þ.

9000 8000 7000

Frame size (byte)

There are a fair amount of work on the application of traffic prediction to wired networks, e.g., bandwidth allocation in ATM networks (e.g., [7,8]), which are not directly relevant to WPANs. The main idea of application of traffic prediction to WPANs proposed in this paper has been filed by the author as an international patent [9]. Independently, in [10] a traffic prediction-based channel time allocation method is proposed for IEEE 802.15.3 wireless personal area networks, where a variable step size prediction algorithm is employed to take into account video scene changes. With regards to MAC and networking issues in WPANs, a superframe formation algorithm for 802.15.3 WPANs is proposed in [11]. Rhee et al. [12] propose to allocate channel times according to the maximum sizes of I, P, B frames of (predefined) MPEG-4 streams. However in many real-time applications this information is not known in advance, hence their approach is not practical.

M 1 X

6000 5000 4000 3000 2000 1000 0

0

1

2

3

4 5 Frame index

6

7

8

9 x 10

Fig. 4. VBR MPEG-4 traffic – Jurassic Park, source: trace.eas.asu.edu.

4

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Here we apply the normalized least mean square (LMS) algorithm [14] to video traffic prediction in WPANs. The predictor works as follows:

12000 Actual Predicted 10000

frame size (byte)

(1) Start with an initial estimate of the filter coefficients Wð0Þ. (2)For each new data point, update WðnÞ using the following equation: Wðn þ 1Þ ¼ WðnÞ þ leðn  1ÞSðn  1Þ=jjSðn  1Þjj2 ; where l is the step size. If 0 < l < 2, the algorithm will converge in the mean. Compared to the standard LMS algorithm, the normalized LMS algorithm is less sensitive to the step size. For MPEG-4 streams, because I, P, and B frames have different statistical characteristics, we separate them and predict each frame type separately. Simulations using real video traces (presented later on) have shown that the prediction error resembles white noise which is uncorrelated and therefore requires only small buffers. Based on the above prediction, a device can calculate the required number of MASs, taking into account possible frame fragmentation and time intervals such as SIFS (short inter-frame space) and ACKs as specified in the standard [1].

8000

6000

4000

2000

0 50

100

150

200 frame

250

300

350

Fig. 5. Actual and predicted I frames for the Lambs trace.

1

4. Performance evaluation 0.8

0.6

0.4 ACF

Here we used real MPEG-4 traffic traces available from [15]. Six MPEG-4 encoded, medium quality video traces of popular movies have been chosen, each with a length of 60 min and a frame rate of 25 frames/s: Jurassic Park I (Park), Silence of the Lambs (Lambs), Star Wars IV (Star), Star Trek – First Contact (First), Die Hard III (Die), and Aladdin. Table 1 summarizes their statistics: mean, minimum and maximum frame sizes (in byte). It is evident that MPEG4 traces have highly variable frame sizes (and bit rates) and are very bursty. It has been found by experiments that the choice of M is quite robust in terms of prediction errors. In the results reported below we use M = 12, which is a good choice according to the Akaike information criterion [7]. Similarly, we have found l ¼ 0:2 which is good enough for most cases except the Star Wars trace. We will elaborate more on this later in this section. Fig. 5 shows the actual and predicted sequences of the I frames of Silence of the Lambs. Fig. 6 presents the autocorrelation function of the prediction error. It is observed that in contrast to the original, highly correlated video trace the error process resembles an uncorrelated white noise, hence requiring only small buffer spaces. This is also confirmed by simulations later in this section. Table 2 tabulates the prediction results for all the six traces, with I, P, B subsequences listed separately. The performance metric is relative mean squared error (RMSE) or inverse signal-to-noise ratio (SNR1):

0.2

0

-0.2

-0.4

0

1000

2000

3000

4000 lag

5000

6000

7000

8000

Fig. 6. Autocorrelation of prediction error.

From Table 2, we can see that the linear predictor works very well for MEPG-4 traces, with RMSE values in a similar range of those reported in a previous study [7] for MPEG-1 traffic. Predictions of I

frames generally have the best performance, while P frames are much more difficult to predict. Another interesting observation is that the Star Wars trace seems to be the most challenging to predict, with quite high RMSE for P frame prediction. A tentative explanation is that, because of the existence of plenty of highly dynamic actions in the movie there are many sudden scene changes, resulting in large prediction errors. We have tuned the two predictor parameters l and M to study their impacts on prediction performance. Fig. 7 shows that for this particular trace, RMSE increases as M decreases or l increases. When M is small, l has a strong influence on the performance. On the other hand, the larger l becomes, the more obvious the impact of M shows. With l fixed, RMSE does not change significantly beyond M = 12, indicating

Table 1 Trace statistics.

Table 2 Prediction results.

RMSE ¼

X

, eðnÞ

n

Mean Min Max

2

X

sðnÞ2 :

n

Park

Lambs

Star

First

Die

Aladdin

1300 26 8511

880 28 11915

390 26 4690

540 26 5945

1200 26 8161

770 26 6735

I P B

Park

Lambs

Star

First

Die

Aladdin

0.0160 0.0912 0.0415

0.0499 0.1737 0.0406

0.0220 0.7968 0.2861

0.0354 0.1531 0.0592

0.0412 0.1211 0.0397

0.0383 0.2189 0.1721

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2 1.8 1.6

RMSE

1.4 1.2 1 0.8 0.6 0.4 0.2 0.25 0.2

20 0.15 µ

15 0.1

10 0.05

5 0

M

0

Fig. 7. RMSE vs. M and

l.

1.8 1.6

1.4

1.2 RMSE

12 is a good choice for M. Fig. 8 demonstrates that for fixed M of 12, RMSE drops almost linearly as l decreases, with its minimum reaching 0.2245 when l is 0.01. So when the step size is properly chosen, Star Wars can be predicted with reasonable accuracy. In summary, the normalized LMS algorithm is able to predict a wide range of MPEG-4 traffic accurately enough to be used for bandwidth allocation in WPANs. Next we compare the performance of bandwidth allocation schemes based on our traffic prediction with that of peak rate allocation and mean rate allocation. Any one of the six MPEG-4 video streams is transmitted from a source to a destination in a WPAN. For the sake of simplicity, we ignore the various standard-specific MAC overheads and concentrate on bandwidth allocation efficiency. The performance metrics are link utilization and packet loss [10]. The buffer size is set at 300 bytes. Table 3 tabulates the channel utilization of the three different schemes. Prediction-based scheme provides the highest link utilization (>90%), while peak rate allocation is the poorest which wastes a lot of bandwidth. This is because VBR video is highly bursty compared to CBR traffic. On the other hand, mean rate allocation has medium utilization but can lead to deteriorated video quality. This is demonstrated by the average loss rate (due to buffer overflow) of video packets shown in Table 4. For all the video streams, mean rate allocation suffers around 30% packet loss rate. Increasing buffer sizes can improve the situation, but obviously at a higher hardware cost. In the above experiments, we fixed M and l as 12 and 0.2, respectively. Although the Star Wars trace is challenging to predict as discussed previously, from Tables 3 and 4 we have observed that channel utilization and loss rate can still be greatly improved with traffic prediction by the simple normalized LMS algorithm. This illustrates the robustness of the proposed prediction method. In contrast to the findings of [10], although the prediction errors of simple prediction algorithms may be high for certain video traces (e.g., due to scene changes), the improvement of network performance is still remarkable. The proposed bandwidth allocation framework is flexible in that various traffic prediction techniques can be applied here according to the trade-off between performance improvement and implementation complexity. For example, instead of predicting each frame type separately, we can predict the rate of the next GOP (the sum of all the frame rates in a GOP) [7]. Further, it has been observed that I frames are the most important MPEG frames that contain the bulk of the video frame data. When I frame size changes significantly, P and B frame sizes also change greatly. Thus,

1

0.8

0.6

0.4

0.2

0

0.05

0.1

0.15

0.2

0.25 µ

Fig. 8. RMSE vs.

0.3

0.35

0.4

0.45

0.5

l (M = 12).

Table 3 Link utilization.

Prediction Peak Mean

Park (%)

Lambs (%)

First (%)

Star (%)

Die (%)

Aladdin (%)

94 16 70

94 7 61

94 9 66

92 8 62

94 15 70

89 12 66

Park (%)

Lambs (%)

Star (%)

First (%)

Die (%)

Aladdin (%)

3.8 32

4 38

4.3 36

3.5 34

4.6 32

7.2 35

Table 4 Average packet loss rate.

Prediction Mean

we can use the LMS predictor to predict only the I frame of the next GOP, which is often the largest in a GOP. 5. Conclusion The new ECMA-368 MAC for UWB WPANs consists of two channel access methods: DRP and PCA. The exact method of medium access slot allocation in DRP is not specified in the standard. This paper describes a distributed MAS allocation method in which a number of interference–avoidance rules are used to reserve slots for devices. Devices maintain sending and receiving tables to track activities in the neighbourhood. A three-way handshake procedure is used for reservation establishment. The number of reserved MASs is derived from the application bandwidth requirement and other system parameters. The proposed method is a simple, effective solution to the DRP MAS allocation problem, avoiding reservation conflicts and interference from hidden terminals. A prediction-based dynamic bandwidth allocation method has also been proposed for WPAN networks. The simple LMS predictor has been shown to be able to predict various VBR MPEG-4 traffic streams accurately. The bandwidth allocation scheme based on traffic prediction yields high channel utilization and low packet loss rate, hence providing good QoS support for multimedia applications in WPANs. For future work we intend to study in more depth the trade-off between the performance gain of dynamic time slot assignment based on traffic prediction and the potential overhead due to extra signalling and computation. Moreover, we are

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interested in comparing our scheme with other scheduling mechanisms proposed recently in the literature, e.g., [16]. Acknowledgement The author thanks the reviewers for their useful comments that helped to improve the presentation of the paper. Part of this work was presented at the FGCN’07 conference in December 2007. References [1] High rate ultra wideband PHY and MAC standard, ECMA-368, ECMA International, December 2005. [2] IEEE 802.15.3 Working Group, Part 15.3: wireless medium access control (MAC) and physical layer (PHY) specifications for high rate wireless personal area networks (WPANs), Draft Standard, June 2003. [3] I. Jawhar, J. Wu, QoS support in TDMA-based mobile ad hoc networks, J. Comput. Sci. Technol. (2005) 797–810. [4] W. Liao et al., A TDMA-based bandwidth reservation protocol for QoS routing in a wireless mobile ad hoc network, IEEE ICC (2002).

[5] R. Koenen, Overview of the MPEG-4 standard. Available from: http:// www.chiariglione.org/mpeg/standards/mpeg-4/mpeg-4.htm. [6] M. Noe, Study of MPEG-4 traffic over IEEE 802.11 ad hoc networks, MSc Thesis, University of Bristol, 2006. [7] A. Adas, Using adaptive linear prediction to support real-time VBR video under RCBR network service model, IEEE/ACM Trans. Netwk. 6 (5) (1998). [8] Q. Pang et al., Adaptive fuzzy traffic predictor and its applications in ATM networks, IEEE ICC (1998). [9] Z. Fan, Means and method of allocating channel bandwidth, UK patent 0513520.7, July 2005; and US patent 20070002743, May 2006. [10] Y. Tseng et al., Scene-change aware dynamic bandwidth allocation for realtime VBR video transmission over IEEE 802.15.3 wireless home networks, IEEE Trans. Multimedia (2007). April. [11] A. Torok et al., Superframe formation algorithms in 802.15.3 networks, IEEE WCNC (2004). [12] S. Rhee et al., An application-aware MAC scheme for IEEE 802.15.3 high-rate WPAN, IEEE WCNC (2004). [13] J. Cowling, S. Selvakennedy, A detailed investigation of the IEEE 802.11e HCF reference scheduler for VBR traffic, IEEE ICCCN (2004). [14] S. Haykin, Adaptive Filter Theory, Prentice Hall, Englewood Cliffs, 1991. [15] ASU video trace research group. Available from: http://trace.eas.asu.edu/. [16] S. Moradi, V. Wong, Technique to improve MPEG-4 traffic schedulers in IEEE 802.15.3 WPANs, IEEE ICC (2007).