Overload control of massive random access for machine-type communications

Accepted Manuscript

Overload control of massive random access for machine-type communications Woon-Young Yeo, Yong-Hee Jo, Dong-Jun Lee PII: DOI: Reference:

S0957-4174(17)30435-9 10.1016/j.eswa.2017.06.018 ESWA 11389

To appear in:

Expert Systems With Applications

Received date: Revised date: Accepted date:

21 February 2017 22 May 2017 11 June 2017

Please cite this article as: Woon-Young Yeo, Yong-Hee Jo, Dong-Jun Lee, Overload control of massive random access for machine-type communications, Expert Systems With Applications (2017), doi: 10.1016/j.eswa.2017.06.018

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Highlights • We show that random access retransmissions can lead to performance

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degradation. • A Markov model is proposed to evaluate the performance of LTE random access.

• There is an optimal number of retransmissions for massive random access.

• Random access resources are separated into two subsets for MTC overload

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control.

• Conventional and proposed schemes can be used adaptively depending on

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traffic load.

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Overload control of massive random access for machine-type communications

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Woon-Young Yeoa , Yong-Hee Joa , Dong-Jun Leeb,∗ a Department

of Information and Communication Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea b School of Electronics and Information Engineering, Korea Aerospace University, 76 Hanggongdaehak-ro, Deogyang-gu, Goyang-si, Gyeonggi-do 10540, Korea

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Abstract

Traditional cellular systems may not be appropriate to support machine-type communications (MTC) due to a large number of devices and relatively small, infrequent data transmissions. The 3GPP has identified the MTC as an important area of the LTE system and has discussed several mechanisms that control random access (RA) overload caused by massive MTC devices. In this pa-

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per, we show that a retransmission mechanism of RA may lead to performance degradation in an overload situation, and propose two RA solutions that relieve

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the RA overload. Since the RA success probability is closely related with the number of simultaneous RA attempts, the first solution adjusts the maximum number of RA retransmissions to control the amount of RA attempts. The

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second solution separates the RA resources into two subsets that MTC devices can access according to the number of consecutive RA failures and distributes the RA traffic over the two subsets. The two proposed solutions are analyzed

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by a mathematical model assuming a simplified operation, and a more realistic environment is considered by protocol-level simulations. Since the performance

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of the proposed solutions depends on the system configurations and parameters, the base station may adaptively adjust them for an optimal operation. Keywords: Machine-type communications (MTC), LTE random access, ∗ Corresponding

author Email addresses: [email protected] (Woon-Young Yeo), [email protected] (Yong-Hee Jo), [email protected] (Dong-Jun Lee)

Preprint submitted to Journal of LATEX Templates

June 22, 2017

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overload control, Markov chain.

1. Introduction

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Cellular systems are expected to play an important role in the deployment of machine-type communications (MTC) or machine-to-machine (M2M) devices

due to a widely deployed infrastructure and the support of device mobility. 5

However, the cellular systems may not be optimal to support MTC devices

because a large number of MTC devices are expected to be deployed in a specific

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area and most MTC devices transmit and receive a small amount of data. The massive MTC devices would generate a huge amount of information flows that cause contention for radio resources or congestion in the radio access network 10

(RAN).

The 3rd Generation Partnership Project (3GPP) has classified MTC as an important area for the Long-Term Evolution (LTE) and LTE-Advanced sys-

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tems (3GPP TR 37.868, 2011). It also has discussed the traffic characteristics of MTC applications and possible RAN improvements to support MTC. The 15

first priority area for the RAN improvements is the RAN overload control that

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handles the random access (RA) of massive MTC devices. 3GPP has identified several improvements to control RA overload: access class barring, RA resource

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separation, dynamic allocation of RA resources, MTC-specific backoff, slotted access and pull-based schemes (3GPP TR 37.868, 2011; Lin et al., 2014; Laya 20

et al., 2013). The access class barring can reduce the RA load by preventing

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MTC devices from initiating RA, but may increase the RA delay unnecessarily (Laya et al., 2013). The RA resource separation in Lin et al. (2014) gives a

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higher priority to the non-MTC devices by allocating RA resources differently to MTC and non-MTC devices. However, it may provide limited benefits due

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to the reduced RA resources and a low priority for MTC devices. In this paper, we focus on the fact that a retransmission mechanism of the

LTE RA may lead to performance degradation because retransmitted RA attempts increase the possibility of collision, especially in an overload situation.

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This paper presents two possible solutions that can relieve the RA traffic over30

load. First, since the probability of collision is closely related with the number of simultaneous RA attempts, it is reasonable to evaluate the impact of the

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number of RA retransmissions on the RA performance. Second, this paper proposes a resource separation scheme where, unlike the conventional RA resource separation between MTC and non-MTC devices, RA resources for MTC devices 35

are separated into two subsets that MTC devices can access according to the number of consecutive RA failures. The two solutions are analyzed by a math-

2. Random access procedure

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ematical model and a more realistic environment is considered in simulations.

In LTE, the RA is an important procedure to establish wireless links between 40

the user equipment (UE) and the network (Dahlman et al., 2011). When a UE has packets to transmit, it performs the RA through the physical random access

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channel (PRACH) in an allowable access slot. The RA process is triggered by a UE for several purposes, including 1) to establish a radio link for initial access to the network, 2) to re-establish a radio link after radio-link failure, 3) to support handover between base stations, 4) to establish uplink (UL) synchronization if

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the UL is not synchronized when data are ready to transmit, and 5) to request

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UL radio resource when no UL resource has been configured on the physical uplink control channel. There are two different RA procedures defined for LTE: contention-free and contention-based. Contention-free RA is used for handover and re-establishing

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UL synchronization upon downlink data arrival, and the base station (BS) signals a dedicated RA preamble for the UE to avoid contention. In contention-

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based RA, UEs need to participate in contention for RA resources. A UE initiates contention-based RA by randomly choosing one preamble on PRACH.

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In this paper, we concentrate on the contention-based RA because MTC devices are expected to transmit packets in a bursty manner and do not have to maintain the wireless link for a long time.

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2. Random Access Response

4. Contention Resolution

UE

1. PRACH Preamble

3. Connection Request

Figure 1: LTE random access procedure.

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BS

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Figure 1 illustrates the contention-based RA procedure, which consists of four steps between the UE and the BS: 60

• Step 1 (Preamble transmission): A UE randomly selects a preamble sequence and transmits it on PRACH. If two or more devices simultaneously transmit the same preamble, collision occurs and further contention reso-

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lution is required in the next steps.

• Step 2 (Random access response): If a BS detects a certain preamble, 65

it transmits a random access response (RAR) that contains the detected

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preamble identity (ID), a UL scheduling grant for Step 3, UL timing information for synchronization and a temporary UE ID for further com-

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munication.

• Step 3 (Connection request): If the UE receives an RAR containing a preamble ID that matches its own preamble sent in Step 1, it transmits

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its unique UE ID to the BS. This unique ID allows the BS to distinguish

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UEs that have sent the same preamble.

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• Step 4 (Contention resolution): If the BS can decode the Step 3 message, it replies with the unique UE ID and only one of the colliding UEs will succeed in the RA. This step will resolve any contention caused by multiple UEs that have sent the same RA preamble in Step 1. PRACH is defined by an RA slot, which is composed of 72 subcarriers (1.08 5

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MHz) and one subframe (1 ms). The BS broadcasts the periodicity of the RA slots by means of a system parameter referred to as the PRACH Configuration 80

Index. The periodicity of the RA slot varies from 1 RA slot per 20 ms to 1 RA

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slot per 1 ms (3GPP TS 36.211, 2015). The LTE provides 64 preamble sequences, which are divided into contentionfree and contention-based RA preambles. The RA preambles for contentionbased RA can be separated further into two preamble groups (A and B) de85

pending on a size of the Step 3 message and a wireless link condition. A UE

selects one of preambles in group B if the message size in Step 3 is greater

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than a certain threshold and the pathloss is less than a certain value (3GPP TS 36.321, 2016). Otherwise, it will select one of preambles in group A. The

available sets of preamble sequences are signaled as part of system information 90

(3GPP TS 36.331, 2016). For example, numberOfRA-Preambles is the number of contention-based RA preambles, sizeOfRA-PreamblesGroupA is the number of RA preambles for group A and messageSizeGroupA is a threshold size of the

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Step 3 message for the preamble group selection. If the preamble configuration for group A is not signaled, RA preambles for group B does not exist. After transmitting a preamble sequence, the UE waits for the RAR message

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containing the preamble ID that has been sent in Step 1 within a configured RAR time window. If the UE does not receive the RAR within the window due

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to preamble collision or link error, the RA is considered to have failed. Additionally, in Step 4, the UEs that do not detect their IDs also declare the previous RA unsuccessful. If RA fails, the UE can retransmit an RA preamble again. To

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avoid contention and overload, the UE retransmits the preamble after a random backoff delay. The RAR message in Step 2 may include a backoff indicator (BI),

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ranging from 0 to 960 ms, to randomize the retransmission timing. All UEs that have sent their preambles in Step 1 can receive the same BI in the RAR and

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the actual backoff period is determined randomly between 0 and BI. When the number of preamble transmissions reaches the maximum allowed value, the RA failure is indicated to upper layers.

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3. Overload control for massive random access The LTE RA suffers from congestion when a large number of UEs try to 110

access the network at the same time because the collision probability rapidly

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grows with the number of contending UEs. 3GPP has identified several im-

provements to deal with the network overload (3GPP TR 37.868, 2011) and a comprehensive survey for overload control has been summarized in Laya et al. (2013).

In LTE, all UEs are members of one out of 10 randomly allocated categories

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defined as access classes (ACs) 0 to 9, and some UEs may be members of one or

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more out of 5 special categories (ACs 11 to 15) for high priority users (3GPP TS

22.011, 2015). The access class barring (ACB) is used to control RA attempts of UEs in the case of network overload. The BS transmits a set of parameters 120

related to ACB as part of system information including a barring factor and barring time. Each UE that wants to access the network will draw a random

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number between 0 and 1; if it is lower than the barring factor, the device is able to begin RA. Otherwise, the access is barred. The barred UE again draws a random number to calculate a backoff time, after which it begins RA again. The barring time broadcast by the BS is the average mean backoff time, ranging

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from 4 to 512 s. Even though ACB can improve the RA throughput, the barring

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factor in the case of congestion may be set to a very small value, which increases the access delay.

Extended access barring (EAB) is evolved from ACB in 3GPP Release 11 and used to explicitly control the access from UEs having a low access priority (3GPP

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TS 22.011, 2015). In congestion, the BS can restrict access from UEs configured as EAB. The BS sends a barring bitmap, consisting of 10 bits numbered 0 to

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9 for different ACs. The UE configured as EAB reads the system information and compares the detected bitmap to its own AC. If the AC and the broadcast

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bitmap match, the UE will not initiate RA until the EAB system information changes. Note that the UEs belonging to the barred ACs are completely barred in EAB, whereas all UEs are barred based on a probability model in ACB. As

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in the ACB case, the access delay can severely increase for the UEs configured as EAB. The RA resource separation can be achieved either by splitting the preambles

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into MTC and non-MTC groups or by allocating RA slots in time/frequency domain differently to MTC and non-MTC groups (3GPP TR 37.868, 2011). In

general, non-MTC devices are able to access all RA resources, whereas MTC

devices are restricted to the pre-defined resource subsets (Laya et al., 2013). 145

This policy gives a higher priority to the non-MTC devices, but it can provide limited benefits because a large number of MTC devices need to share the

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reduced RA resources.

As a hybrid overload control algorithm, a prioritized random access scheme combines resource separation and dynamic access barring (Lin et al., 2014). The 150

scheme divides MTC and non-MTC traffic into five different classes according to a priority and traffic characteristics, and each class can only access a limited set of RA resources. The BS continuously monitors the RA loading states and

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sets the high-loading flag to 1 on a downlink signaling channel when a level of traffic loading is high. If the MTC devices receive a high-loading flag of 1, they will delay their RA attempts for a certain period time. RA attempts from MTC

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devices may be delayed until the high-loading flag is reset to 0. Other overload control methods identified in 3GPP TR 37.868 (2011) in-

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clude dynamic allocation of RA resources, MTC specific backoff, slotted access and pull based schemes. In dynamic allocation of RA resource, the network dynamically adjusts the amount of RA resource based on the RA traffic load.

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A drawback of this scheme is that radio resources for user data need to be converted to PRACH additionally in high traffic load. An MTC specific backoff

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scheme assigns a large backoff window to MTC devices, while assigning a small backoff window to non-MTC devices. This scheme can provide some improve-

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ments for low traffic load, but cannot handle the massive RA arrivals from MTC devices. The slotted access scheme defines the access slots having a specific access cycle for MTC devices. Each MTC device is associated with an access slot through its ID and only accesses the network at its dedicated access slot. A 8

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long access cycle is required to support a large number of MTC devices, and 170

thus can lead to a long access delay. The pull-based scheme can be used if an MTC server is aware of when MTC devices have data to send. The MTC server

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requests the BS to send a paging message to the corresponding MTC device, and the paged device will perform the RA accordingly. This scheme is suitable

for the case where the MTC devices periodically transmit data to the MTC 175

server.

4. Motivation and overload control solutions

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3GPP has defined two traffic models to evaluate the performance of overload control schemes under different MTC access intensities. In traffic model 1, MTC devices access the network uniformly over T = 60 s. In traffic model 2, MTC 180

devices access the network following a beta distribution over T = 10 s. Traffic model 2 is considered as an extreme scenario in which a large number of MTC

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devices access the network in a highly synchronized manner, for example, after a power outage (3GPP TR 37.868, 2011). The RA overload control is essential in traffic model 2 due to a sharp increase in the number of RA attempts. In this paper, traffic model 2 is mainly considered. In addition, only one preamble

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group (i.e., group A) is assumed for the contention-based RA, regardless of the

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message size in Step 3 of the RA procedure. Figure 2(a) depicts the number of RA attempts in every RA slot of a 5 ms repetition period with traffic model 2 and 30,000 MTC devices. In the figure, many MTC devices (up to 50) try to access the network at the same time within

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each RA slot. If the number of available RA preambles per RA slot is 54, it may seem that the RA resources are enough to handle the RA demand in Figure 2(a).

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However, it should be noted that Figure 2(a) only shows new RA attempts and does not include retransmissions caused by RA collisions. Figure 2(b) shows the

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simultaneous RA attempts when the RA retransmission mechanism is applied to the RA traffic in Figure 2(a) based on simulation parameters in 3GPP TR 37.868 (2011). The number of simultaneous RA attempts easily exceeds the

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40

30

20

10

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number of simultaneous RA attempts

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0 0

2,000

4,000

6,000

8,000

10,000

time (ms)

(a) An RA traffic sample with a beta distribution (traffic model 2, without retransmission).

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RA pattern with retransmission

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number of simultaneous RA attempts

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RA pattern without retransmission

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0 0

2,000

4,000

6,000

8,000

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time (ms)

(b) Comparison of RA patterns with and without RA retransmission. Up to 10 RA attempts are assumed for RA retransmission.

Figure 2: Number of simultaneous RA attempts within every 5 ms with traffic model 2 and 30,000 MTC devices.

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given RA resources in one RA slot and more than 300 RA attempts can be observed on some RA slots. The RA retransmission mechanism may lead to performance degradation

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because it increases the number of simultaneous RA attempts, especially in an overload situation. In this paper, two possible overload control solutions are

considered to relieve the RA traffic overload. The first solution is to adjust the number of RA attempts to reduce the effective number of simultaneous 205

RA attempts. The second solution is a resource separation scheme where RA resources are separated into two subsets that MTC devices can access according

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to the number of consecutive RA failures.

4.1. Adjustment of the number of retransmissions

Contention-based RA may suffer from serious performance degradation in 210

an overload situation. To efficiently handle the heavy RA load from massive MTC devices, it is necessary to analyze the contention and collision patterns

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that cause RA overload. In Lin et al. (2014), the expected number of successful RA attempts per RA slot, S, is given by S = n(1 − 1/M )n−1 for n simultaneous 215

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RA attempts and M preambles. As n increases, S increases and reaches its maximum (e.g., channel efficiency, S/M , of 37.1% for M = 54) at a certain value of n, above which S decreases due to collisions among the RA attempts.

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The RA retransmission mechanism can increase the number of simultaneous RA attempts, and thus S will deteriorate in the overload case. In Phuyal et al. (2012), the combined number of new and retransmitted attempts at the most congested RA slot can be more than four times the number of new RA

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attempts only and the instantaneous collision probability exceeds 99%. Thus,

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the RA retransmission may have a negative impact on RA performance even though it has been devised to increase the RA success probability.

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Since the number of simultaneous RA attempts is closely related with the

RA performance, it is worthwhile to estimate the RA performance by controlling the number of retransmissions. A small number of retransmissions may improve the overall RA performance in the overload situation because of the reduced 11

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number of simultaneous RA attempts. Thus, in this paper, the RA performance is evaluated by adjusting the number of RA attempts and we will discuss the optimal number of RA attempts. 4.2. Resource separation for retransmissions

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Since all devices share a single set of RA resources, they all suffer from RA

overload caused by excessive retransmissions. In this paper, we propose a re-

source separation scheme for RA overload control by separating RA resources 235

into two subsets that MTC devices can access according to the number of con-

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secutive RA failures. If the RA resources for MTC devices are separated into

two subsets, RA traffic can be distributed over the two RA subsets differently and at least one of the two subsets can be controlled not to be congested. M preambles

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Separation mode

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Normal mode

Phase-1 RA subset (M-m preambles)

Triggered by overload detection

Phase-2 RA subset (m preambles)

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Transition to Phase-2 subset after K consecutive RA failures

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Figure 3: Proposed RA resource separation for overload control.

Figure 3 shows the conceptual operation of the proposed scheme. There

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are two sharing modes of RA preambles: a normal mode when the RA load

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is under a certain level and a separation mode when an RA overload occurs. In the normal mode, each MTC device selects one of M preambles as in the

LTE RA. In the separation mode, the RA preambles are divided into Phase-1 and Phase-2 subsets, composed of M − m and m preambles, respectively. Each

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MTC device determines its RA subset based on the number of consecutive RA failures. The MTC device selects one of the Phase-1 preambles if the number 12

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of consecutive failures is equal to or smaller than K, whereas it selects one of the Phase-2 preambles for more than K consecutive failures. The RA load in the two subsets can be adjusted by the combination of K and m; thus, it is possible to set the parameters to relieve the RA overload in at least one of the

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subsets. Since each MTC device has a chance to access both of the two subsets

on consecutive failures, it is possible to perform the RA in the under-loaded RA subset, resulting in a higher success probability than the LTE RA. The

combination of K and m has an impact on the RA performance, and thus the 255

BS can optimize the combination of K and m based on the RA traffic load.

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The transition to the separation mode is triggered by overload detection

at the BS and can be signaled as part of system information. The BS can monitor the number of successful RA attempts at each RA slot and decide a high-loading situation based on the changing pattern of the monitored value 260

(Lin et al., 2014). Another possible method is that each MTC device informs the BS about the number of RA attempts by using the Step 3 message. The

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BS can estimate the UL overload by monitoring the reported values. A possible mode transition policy will be described after analyzing the proposed resource

5. Analysis model for random access

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separation in Section 6.

We derive RA channel efficiency and success probability for the LTE and resource separation schemes. Since the entire RA procedure is too complex

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to be formulated, we assume that there are N MTC devices and the RA is

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successful if a unique preamble is selected by a single MTC device. success 1−q

1−p

Idle

p

Contention

failure; r

q

ReTx

failure; 1−r

Figure 4: Probabilistic model for random access.

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Figure 4 shows the assumed RA model for the LTE and resource separation

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schemes. Each MTC device in an idle state begins a new RA with probability p in each RA slot and selects one of the available RA preambles. If two or

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more devices select the same preamble, a collision occurs at the BS and the colliding devices go to a retransmission state (ReTx). Each MTC device in 275

ReTx retransmits a preamble with probability q in each RA slot. Generally,

q is set to be greater than p for fast retransmission. To limit the number of preamble transmissions, this model introduces a return probability r, with which the colliding devices return to the idle state. Thus, the average number of

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preamble transmissions will be 1/r on consecutive RA failures. In the proposed resource separation, it is assumed that only new RA attempts use the Phase-1 RA subset and MTC devices in ReTx use the Phase-2 RA subset (i.e., K = 1). Let Xn denote the number of MTC devices in ReTx at the n-th RA slot. This model can be considered as a Markov chain because Xn+1 depends only on the current state Xn . To obtain a steady-state probability, we need to know the transition probability pxy = Pr(Xn+1 = y|Xn = x). Let U and V be the

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numbers of devices that transmit a preamble at the beginning of the (n+1)-

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th RA slot in the idle and ReTx states, respectively. Next, T and W are the numbers of devices that succeed in RA among the U and V devices, respectively. By conditioning on U , V , T and W , X

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Pr(Xn+1 = y|w, t, v, u, x)

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∀u,v,t,w

×Pr(T = t, W = w|v, u, x) · Pr(U = u, V = v|x).

(1)

Before proceeding further, we need to obtain the number of combinations

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that k MTC devices select l different preambles so that each preamble is selected

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by at least two devices or is not selected by any device. The number of these combinations, f (l, k), is given by applying the cardinality of sets (Duan et al., 2013): min(l,k)

f (l, k) =

X c=0

   l k (−1)c   c!(l − c)k−c . c c 14

(2)

N−x (idle)

x (ReTx)

u attempts

v attempts

t u−t v−w w successes failures failures successes g returns to the idle state

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Figure 5: State variables in the probabilistic model.

f (l, k) will be used later to obtain all possible combinations of successful RA

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attempts.

5.1. State transition probability for LTE random access

In LTE, RA preambles are shared by all MTC devices in the idle and ReTx states. Let M be the number of available RA preambles for MTC devices and G be the number of devices that return to the idle state due to the return

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probability r for the failed RA attempts. Figure 5 shows the state variables in

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this probabilistic model, given Xn = x. Among the RA attempts of U = u and V = v in the idle and ReTx states, respectively, t+w RA attempts are successful and u + v − t − w RA attempts are unsuccessful. Among the u + v − t − w failed RA attempts, g attempts return to the idle state. Since the next state y is

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expressed as y = x + u − t − w − g given G = g, Pr(Xn+1 = y|w, t, v, u, x) in (1)

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is given by

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Pr(Xn+1 = y|w, t, v, u, x) = Pr(G = x + u − y − t − w|w, t, v, u, x)   u+v−t−w  rx+u−y−t−w (1 − r)v−x+y . = x+u−y−t−w

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(3)

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Pr(T = t, W = w|v, u, x) can be obtained by considering all possible combinations of T + W successes. By (2),    u v (t + w)!    Pr(T = t, W = w|v, u, x) = u+v M t+w t w M

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

×f (M − t − w, u + v − t − w).

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Next, since U and V are independent random variables,

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Pr(U = u, V = v|x) = Pr(U = u|x) · Pr(V = v|x)     N −x u x p (1 − p)N −x−u  q v (1 − q)x−v . = u v

(4)

(5)

The valid ranges of the variables are 0 ≤ x ≤ N , 0 ≤ y ≤ N , max(0, y − x) ≤ u ≤ N − x, max(0, x − y) ≤ v ≤ x and {(t, w)|0 ≤ t ≤ u, 0 ≤ w ≤ v, 0 ≤ t + w ≤ min(M, x + u − y)}.

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5.2. State transition probability for resource separation

In the proposed resource separation, each preamble subset is accessed by

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idle or retransmitting devices. Due to the similarity to LTE analysis, we only need to modify (4). Assume that m out of the M preambles are allocated to the devices in ReTx. Since the RA attempts in the idle and ReTx states are

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independent,

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Pr(T = t, W = w|v, u, x)    M −m u 1   t!f (M − m − t, u − t) = u (M−m) t t    m v 1 × v   w!f (m − w, v − w). m w w

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(6)

The valid ranges of the variables are the same as those in the LTE analysis, except {(t, w)|0 ≤ t ≤ min(M −m, u), 0 ≤ w ≤ min(m, v), 0 ≤ t+w ≤ x+u−y}.

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5.3. RA efficiency and success probability Since the proposed Markov model is irreducible, aperiodic and positive re-

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current, there will be a unique steady-state probability πx for a state x. {πx } P can be obtained by solving a set of linear equations, πy = ∀x pxy πx and P ∀x πx = 1. Let Nn , Nr and Ns denote the numbers of new, retransmitted and successful

RA attempts in an RA slot, respectively. The expectation of Nn , E[Nn ], can be obtained by πx

x=0 330

(N −x X

)

u Pr(U = u|x) .

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E[Nn ] =

N X

Similarly, the expectation of Nr , E[Nr ], is given by ( x ) N X X πx v Pr(V = v|x) . E[Nr ] = x=0

(7)

(8)

v=0

Next, the conditioned expectation of Ns , E[Ns |v, u, x], is v u X X

(t + w) Pr(T = t,W = w|v, u, x),

(9)

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E[Ns |v, u, x] =

t=0 w=0

where Pr(T = t, W = w|v, u, x) is given by (4) for the LTE case and (6) for the

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resource separation, respectively. Then, (N −x x ) N X XX E[Ns ]= πx E[Ns |v,u,x] Pr(U = u,V = v|x) . x=0

(10)

u=0 v=0

attempts to the total number of preambles in an RA slot, and given by

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335

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Now, the channel efficiency, ηc , is defined as the ratio of the successful RA

ηc = E[Ns ]/M.

(11)

The access efficiency, ηa , is defined as the ratio of the successful RA attempts

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to the total number of RA attempts in an RA slot. ηa = E[Ns ]/(E[Nn ] + E[Nr ]).

(12)

In addition, the RA success probability, ps , is defined as the probability that a new RA attempt is successful, and ps = E[Ns ]/E[Nn ]. 17

(13)

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5.4. Performance evaluation Since the number of Markov states is equal to the number of MTC devices, a large number of states up to 30,000 can make it difficult to solve the steady

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state probabilities. We assume 100 MTC devices and 10 preambles in order to focus on the operational characteristics of the conventional and proposed RA 345

schemes with reasonable calculation time. Computer simulation is performed to validate the accuracy of this analysis with the same system parameters as the mathematical model.

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5.4.1. Adjustment of the number of retransmissions

Figure 6 shows the RA channel efficiency and success probability for the 350

original RA scheme. The retransmission probability q in a ReTx state is fixed to 0.3, but the performance for other q is similar to the patterns in Figure 6. Lines represent theoretical results and symbols represent simulation results. The figure shows that the simulation results coincide well with the theoretical

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results.

The channel efficiency ηc in Figure 6(a) shows the typical characteristics of

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ALOHA-like random access. As the offered load increases, ηc increases to the maximum value (about 36.8 % ' 1/e) and then falls rapidly due to collision. The traffic load adjusted by p can be divided into three regions based on the

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resulting efficiency patterns: light, heavy and extra-heavy traffic loads. In the light traffic load with p smaller than about 0.05, as the offered load increases,

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ηc also increases because the number of RA preambles is much greater than the number of RA attempts and the possibility of collision is very small. More RA retransmissions with smaller r can increase ηc further in this region. In the

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heavy load with p between about 0.05 and 0.3, ηc decreases significantly as p

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increases because the collision probability rapidly grows with increasing traffic load. In addition, as the number of retransmissions increases by reducing r, the number of simultaneous RA attempts increases further and more collisions occur. Thus, in this region, the RA retransmission can have a negative impact on the RA performance. Note that all the curves cross the same point at p = 0.3. 18

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r = 0.01 r = 0.05 r = 0.1 r = 0.2 r = 0.5

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channel efficiency,

c (%)

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(a) Channel efficiency ηc for original random access.

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r = 0.01 r = 0.05 r = 0.1 r = 0.2 r = 0.5

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access efficiency,

a

(%)

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(b) Access efficiency ηa for original random access.

0.8

r = 0.01 r = 0.05 r = 0.1 r = 0.2 r = 0.5

0.6

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RA success probability,

ps

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1.0

0.4

0.2

0.0 10

-3

10

-2

p

10

-1

10

0

(c) Success probability ps for original random access. Figure 6: RA efficiency and success probability for LTE random access (N = 100, M = 10, q = 0.3). Lines represent theoretical results and symbols represent simulation results.

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The reason is that all new and retransmitting MTC devices attempt the RA with the same probability (p = q = 0.3), regardless of r. Finally, in the extraheavy traffic load with p greater than 0.3, ηc is still decreasing with increasing

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traffic load, but the decreasing pattern is reversed for r. If p is greater than q, MTC devices in the idle state begin the RA more frequently than those in the 375

ReTx state. Most of MTC devices in the idle state will go to the ReTx state after their first RA in this region, and more MTC devices stay in the ReTx state as the number of retransmissions increases by reducing r. Therefore, small r

reduces the number of simultaneous RA attempts at fixed p and q in this extra-

380

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heavy traffic load. However, this extra-heavy region with p > q is unrealistic because the retransmission interval is longer than the inter-arrival time of the newly generated RA for a single MTC device.

Figure 6(b) shows the access efficiency ηa for different r, with the same parameters as in Figure 6(a). As the offered load increases, ηa steadily decreases because the number of new and retransmitted RA attempts increases. In the light and heavy traffic load with p smaller than about 0.3, ηa becomes lower

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by increasing the number of retransmissions (i.e., by reducing r). In the extra-

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heavy traffic load with p greater than 0.3, the decreasing pattern of ηa is reversed for r because E[Ns ] is higher for smaller r and the number of RA attempts does not increase significantly in this region. Figure 6(c) shows the RA success probability ps for different r. As the offered

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load increases, ps steadily decreases. It is important to note that ps is defined

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as E[Ns ]/E[Nn ] in this analysis model. Since most of MTC devices are in the ReTx state for the heavy and extra-heavy traffic loads, the number of new RA attempts, E[Nn ], is small and becomes even smaller as r decreases.

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From Figure 6, we can see that the channel efficiency shows dynamic per-

formance patterns and can give more insight into the RA performance than the access efficiency and the success probability. Figure 7 shows the RA channel efficiency by adjusting the average number of

RA retransmissions (= 1/r) when the offered traffic is fixed. In this figure, the 400

performance for the extra-heavy load is not shown because it is an unrealistic 20

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p = 0.01 p = 0.02 p = 0.05 p = 0.1 p = 0.2

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20

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-1

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channel efficiency,

c (%)

40

10

10

0

r

Figure 7: RA channel efficiency with increasing r or decreasing the average number of retransmissions. N = 100, M = 10 and q = 0.3. Lines represent theoretical results and symbols

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represent simulation results.

assumption. As the average number of retransmissions increases by reducing r,

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ηc slightly increases in the light traffic load (p = 0.01, 0.02 and 0.05), whereas ηc in the heavy load decreases (p = 0.1 and 0.2) due to severe collisions caused by retransmissions. The RA retransmission mechanism slightly improves ηc in the light traffic load (up to 20% gain), but can make the performance worse

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405

in the heavy traffic load (up to 49% loss). Again, we can see that the RA retransmission mechanism may have a negative impact on the RA performance

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in the heavy traffic load.

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5.4.2. Resource separation for retransmissions

410

The RA channel efficiency ηc of the proposed resource separation is shown

in Figure 8 with increases in the offered traffic load. To give a typical example, q and r are selected as 0.3 and 0.5, respectively. When the offered load is low, ηc

of the resource separation is equal to or lower than that of LTE. The possibility of collision is low at the light load, but the resource separation may lead to

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(%)

40

LTE random access

c

resource separation (m= 1) resource separation (m= 2) resource separation (m= 3) resource separation (m= 4)

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channel efficiency,

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(a) RA channel efficiency for resource separation with m = 1 to 4.

(%)

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LTE random access

channel efficiency,

resource separation (m=6) resource separation (m=7) resource separation (m=8)

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resource separation (m=9)

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c

resource separation (m=5) 30

p

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(b) RA channel efficiency for resource separation with m = 5 to 9. Figure 8: RA channel efficiencies of the LTE and resource separation schemes for various

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numbers of Phase-2 preambles (m). N = 100, M = 10, q = 0.3 and r = 0.5. Lines represent theoretical results and symbols represent simulation results. Symbols for the LTE RA are

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omitted to improve readability.

unnecessary collisions due to reduced resources in each RA subset. However, the resource separation has higher ηc than LTE at a high load, regardless of

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the number of Phase-2 preambles (m). The proposed scheme can distribute the

RA load over the two RA subsets by adjusting K and m, so that it can relieve

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the RA overload in at least one of them. Since the MTC devices access the two

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subsets one by one on consecutive failures, the proposed resource separation shows a higher RA success probability than the LTE where a single shared set of RA resources is overloaded. Figure 9 shows the channel efficiency of the LTE and resource separation schemes for the light and heavy traffic loads. For the light traffic load of p = 0.05 22

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40

r = 0.1

35

r = 0.5

c (%) channel efficiency,

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r = 0.2

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LTE random access proposed schemes

15 1

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number of preambles for Phase-2 RA, m

(a) RA channel efficiency for p = 0.05 (light traffic load) and q = 0.3.

proposed schemes

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c (%)

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r = 0.5

r = 0.2

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channel efficiency,

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r = 0.1

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10 1

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number of preambles for Phase-2 RA, m

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(b) RA channel efficiency for p = 0.2 (heavy traffic load) and q = 0.3.

Figure 9: Performance comparison with different average numbers of RA attempts adjusted by r (N = 100 and M = 10). Lines represent theoretical results and symbols represent simulation results. Symbols are not shown for the LTE RA to improve readability.

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in Figure 9(a), the LTE has higher ηc at smaller r due to repeated RA attempts and a low possibility of collision. The resource separation has lower ηc than the LTE for all values of m because a small amount of RA resources in each

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subset may lead to unnecessary collisions even in the light traffic load. On the other hand, for the heavy traffic load of p = 0.2 in Figure 9(b), we can observe 430

the opposite pattern. The LTE has lower ηc at smaller r because the number of simultaneous RA attempts increases in the heavy traffic load. The resource separation has higher ηc than the LTE for all values of m. ηc is especially high at

either small or large m. The proposed resource separation achieves up to 78.9%

435

for m = 9 and r = 0.5.

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improvement over LTE for m = 1 and r = 0.2, and up to 48.6% improvement

From Figure 8 and 9, we can see that the proposed resource separation has a performance gain at the high traffic load, whereas the original LTE is suitable for the light traffic load. Thus, the system operator can adaptively use the LTE and resource separation mechanisms based on the mode transition between the normal and separation modes.

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6. Simulations

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Although the simplified analysis model can offer a meaningful insight into

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the performance of the LTE and resource separation schemes, it is worthwhile to consider a more realistic environment by means of the protocol-level simu445

lator recommended in 3GPP TR 37.868 (2011). Of the two RA traffic models

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defined by 3GPP, traffic model 1 assumes that MTC devices access the network uniformly over a distribution period of T = 60 s, whereas traffic model

2 assumes that a large number of MTC devices access the network in a highly

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synchronized manner following a beta distribution over a distribution period of

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T = 10 s. Since traffic model 1 is suitable for a light-load scenario, traffic model 2 is considered in this simulation. A single-cell environment is assumed with a system bandwidth of 5 MHz and the number of available RA preambles (M ) for MTC devices is 54. The preamble

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Table 1: Simulation parameters

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parameters values Cell bandwidth 5 MHz PRACH Configuration Index 6 Total number of preambles for MTC 54 Maximum number of RA attempts 10 (default) Number of UL grants per RAR 3 Number of PDCCHs 4 RAR window size 5 ms Backoff indicator 20 ms HARQ retransmission probability 0.1 Maximum number of HARQ trials 5

detection probability at the BS is 1 − e−i for the i-th preamble transmission in 455

case of no collision. The periodicity of the RA slot is 2 RA slots per 10 ms (PRACH Configuration Index = 6). The maximum number of RA attempts, L, is 10, if not mentioned otherwise. An RAR window is 5 subframes with up to

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3 UL grants per RAR and the backoff indicator is 20 ms. Up to four physical downlink control channels (PDCCHs) are shared by RAR and Step 4 messages. 460

Since Step 4 is the last stage of the RA procedure, we give priority to Step 4

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messages over RAR messages. Up to three PDCCHs can be assigned to Step 4 messages before being assigned to RAR messages. The Step 3 message has a retransmission probability of 0.1, supported by a synchronous non-adaptive

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HARQ operation. The Step 4 message also has a retransmission probability of 0.1, but it is retransmitted by an asynchronous non-adaptive HARQ operation.

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The processing latency and other system parameters are given in 3GPP TR 37.868 (2011) and 3GPP TR 36.912 (2014). All the statistics are collected for the period of time between the activation of the first MTC device and the

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completion of the last RA procedure. The basic simulation parameters are

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summarized in Table 1. In this simulation, the RA success probability ps is used for performance

comparison. ps is defined as the probability to successfully complete the RA procedure within the maximum number of RA attempts. Unlike the analysis

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0.6

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success probability ,

ps

0.9

0.3

N = 20,000 N = 25,000 N = 30,000

1

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0.0 5

6

7

maximum number of RA attempts,

8

9

10

L

Figure 10: The success probability of the LTE RA with increases in the maximum number of RA attempts (L) for N = 20, 000, 25,000 and 30,000.

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model, the RA channel efficiency is proportional to ps in this simulation model because each MTC device begins the RA procedure only once within T = 10 s.

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The channel efficiency ηc is easily obtained by ηc = N ps /{2(T /τ )M }, where N is the number of MTC devices and τ is the length of a radio frame (10 ms). Figure 10 shows the RA success probability of the LTE RA by adjusting

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the maximum number of RA attempts (L). N is selected as 20, 000, 25, 000 and 30,000 to consider the heavy traffic load situations. As L increases, ps ini-

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tially increases and then decreases gradually. The maximum ps is achieved at L = 6, 4 and 3 for N = 20, 000, 25, 000 and 30,000, respectively. We can see that the optimal L achieving the maximum ps decreases as N increases. Even

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though a certain amount of retransmissions can help the successful RA, more

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retransmissions than the optimal value can cause the performance degradation because of more collisions. If the BS can detect the RA overload, it may adaptively adjust the maximum number of RA attempts. Assume that each MTC device informs the BS about the number of RA attempts by using the Step 3

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message. If the reported numbers of RA attempts include a higher value (e.g., 5 490

– 10) more than a certain percentage of reported messages within an averaging window (e.g., 30 – 50 ms), the BS may reduce the maximum number of RA

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attempts by broadcasting system information. Figure 11 shows the RA success probability of the LTE and resource separation schemes with N = 20, 000, 25,000 and 30,000. The LTE shows ps = 0.691, 495

0.448 and 0.314 at N = 20, 000, 25,000 and 30,000, respectively. The resource separation scheme shows widely varying performance depending on the number

of consecutive RA failures for state transition (K) and the number of pream-

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bles for the Phase-2 RA subset (m). At a slightly overloaded condition with

N = 20, 000, the resource separation has higher ps than LTE at either small 500

or large m, although some combinations of K and m give lower ps . At highly overloaded conditions with N = 25, 000 and 30,000, the resource separation has higher ps than the LTE in most cases. The resource separation achieves up to 19.1%, 57.2% and 95.5% improvement for N = 20, 000, 25,000 and 30,000,

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respectively. The simulation results show that the proposed resource separation has a performance benefit under an overload situation.

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Since the success probability widely varies depending on the combination of K and m, it is worthwhile to suggest reasonable combinations of K and m. The following combinations are recommended from Figure 11: small m combined

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with K = {4, 6} and large m combined with K = {4, 6, 8} for N = 20, 000; small m combined with K = {2, 4} and large m combined with K = {6, 8}

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for N = 25, 000 and 30,000. Small m gives a large amount of RA resource to MTC devices in the early stage of the RA cycle, whereas large m gives a large

amount of RA resource to MTC devices with repeated RA failures exceeding

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K. Considering the success probability and operating sensitivity for m, it is

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preferred to set K = 6 for N = 20, 000 and K = 8 for N = 25, 000 and 30,000, with large m (e.g., m = 46). However, energy efficiency may have an impact on the preferred combination of K and m, and will be discussed later in this section. Figure 12 depicts the typical examples of RA patterns for the two solutions, 27

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0.9

resource separation (K = 2),

resource separation (K = 4)

resource separation (K = 6),

resource separation (K = 8)

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(a) RA success probability for N = 20, 000. 0.8

resource separation (K = 2),

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resource separation (K = 6),

resource separation (K = 8)

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number of preambles for Phase-2 RA,

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(b) RA success probability for N = 25, 000.

LTE random access

resource separation (K = 4)

resource separation (K = 6),

resource separation (K = 8)

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success probability ,

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resource separation (K = 2),

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number of preambles for Phase-2 RA,

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m

(c) RA success probability for N = 30, 000.

Figure 11: Comparison of RA success probabilities for the LTE and resource separation schemes by simulation (N = 20, 000, 25, 000 and 30,000).

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showing the simultaneous RA attempts in every RA slot with traffic model 2 and 30,000 MTC devices. Figure 12(a) shows the simultaneous RA attempts by adjusting L (L = 1, 3, 5, 7 and 10). The number of RA attempts rapidly

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increases as L increases because the possibility of collision is high in the heavy traffic load. If L increases by one, the number of simultaneous RA attempts 525

increases by about 30 on average during the peak period of 3,500 – 4,500 ms in this example. Note that the number of RA preambles provided in each RA

slot is 54 and the channel efficiency usually does not exceed 36.8% of ALOHAlike random access. Referring to Figure 10, the RA success probability for

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N = 30, 000 reaches a maximum at L = 3.

Figure 12(b) shows the simultaneous RA attempts for the proposed resource

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separation with {K = 4, m = 5} and {K = 6, m = 45}, which can achieve the success probability improvement of about 80% over the LTE RA at small and large m, respectively, from Figure 11(c). In the proposed scheme, it is possible to distribute the RA traffic intensionally between Phase-1 and Phase-2 RA subsets by adjusting K and m. MTC devices having a small number of

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consecutive RA failures share a large amount of RA resource by setting K = 4

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and m = 5. In this configuration, it is likely that the RA attempt is successful at the early stage of the RA cycle, which can reduce the number of simultaneous RA attempts as shown in Figure 12(b). On the other hand, a combination of K = 6 and m = 45 gives a small amount of RA resource to MTC devices in the

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early stage of the RA cycle, so that most of MTC devices will eventually use

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the Phase-2 RA subset and this combination does not reduce the total number of RA attempts. Figure 13 shows the distribution of the number of RA attempts at N =

30, 000. The statistics are collected for successful MTC devices and averaged

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over 100 RA scenarios. The success probability of the LTE RA is 31.4%, and {K, m} for resource separation is selected from those that show performance

improvement of about 70% over the LTE RA in Figure 11(c). In the LTE RA, the number of RA attempts is distributed between 1 and L = 10 with

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a decreasing pattern. In the resource separation, the RA traffic is distributed 29

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L = 10

300

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L=7

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number of simultaneous RA attempts

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2,000

4,000

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time (ms)

(a) Simultaneous RA attempts according to the maximum numbers of RA attempts (L = 1, 3, 5, 7 and 10).

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resource separation 200

with

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number of simultaneous RA attempts

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K

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= 4,

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= 5

LTE random access including retransmissions

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new RA attempts

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time (ms)

(b) Comparison of simultaneous RA attempts between the LTE and resource separation schemes.

Figure 12: Typical samples of RA patterns for the two overload control solutions, showing the number of simultaneous RA attempts in every 5 ms (N = 30, 000).

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LTE random access 20

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resource separation (K=2, m=8)

occupancy (%)

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number of RA attempts

Figure 13: Distribution of the number of RA attempts for successful MTC devices (N = 30, 000). K and m for resource separation are selected from those that show performance

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improvement of about 70% over the LTE RA.

between Phase-1 and Phase-2 RA subsets by adjusting K and m. For {K = 2,

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m = 8} and {K = 4, m = 6}, a large amount of RA resource is assigned to the Phase-1 RA subset and most of successful RA attempts are concentrated

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between 1 to K. Similarly, for {K = 6, m = 44} and {K = 8, m = 36}, a

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large amount of RA resource is assigned to the Phase-2 RA subset and most of successful RA attempts are concentrated between K + 1 to 10. Figure 14 shows the average access delay and the average number of RA

attempts for the LTE and resource separation schemes. The RA access delay is measured by the time difference between the first RA attempt and the comple-

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Figure 14: Comparison of the average access delay and the average number of RA attempts

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for N = 30, 000. The statistics are collected for all RA attempts including failed RA.

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tion of the RA procedure for all MTC devices. Since the average access delay is roughly proportional to the number of RA attempts, they show a similar pattern in the figure. Moreover, these two measures are closely related with the

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energy consumption for the RA because MTC devices consume more energy as the access delay and the number of RA attempts increase. From the figure, it 565

can be inferred that the energy consumption increases as K and m increase. If the success probability (Figure 11) and energy consumption (Figure 14) are

considered together, it is preferred to use the small values of K and m in the proposed resource separation (e.g., K = 2 and m = 4).

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In the simulation, the proposed resource separation has been applied stati-

cally to the traffic model 2, so that there was no mode transition between the normal and separation modes. Before completing this section, we suggest one of possible mode transition algorithms for the network operators. Starting from the normal mode, the transition to the separation mode can be triggered by overload detection at the BS and signaled as part of system information. We assume that each MTC device informs the BS about the number of RA attempts

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by using the Step 3 message. The overload detection at the BS is based on the

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fact that more retransmissions are observed in traffic congestion. Let I(k) is an indicator function equal to 1 if the BS detects the k-th RA attempt from the

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Step 3 messages within a window size of W1 (e.g., 30 – 50 ms). The BS triggers PL PL a separation mode if k=1 I(k) ≥ H1 and k=H2 I(k) ≥ 1, where H1 and H2 are system parameters that adjust the timing of mode transition. H1 is used to

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collect enough data for decision making (e.g., H1 = 4). H2 is the minimum RA attempts that can be interpreted as an RA overload (e.g., H2 = 5). Next, the BS can detect the end of congestion when most of RA attempts are successful in the early stage of the RA procedure. The BS returns to the normal mode if PL k=H3 I(k) = 0 for a period of W2 (e.g., 100 ms). H3 is the minimum number

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of RA attempts that is not observed in the light RA load (e.g., H3 = 4). This mode transition algorithm can be applied to the simulation model and the starting and ending times of the separation mode are easily adjustable. However,

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in the simulation, the RA performance with this mode transition is almost the 33

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same as that with separation mode only because traffic model 2 is an extreme scenario where most of RA attempts are concentrated in an overload period. Since the RA performance and energy consumption vary widely depending

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on the operating parameters, it is desirable that the BS makes a decision on the optimal system parameters by gathering and analyzing the collected data, and has the ability to adapt to a desirable operation. A typical set of system parameters can be extracted from the simulation with traffic model 2, but it

is necessary to estimate the RA performance with various traffic models under

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realistic MTC environments.

7. Conclusions

This paper has proposed two RA overload control solutions for the massive machine-type communications. Since the retransmission mechanism of the LTE RA may lead to performance degradation due to the high possibility of collision

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in an overload situation, the proposed solutions were devised to reduce the number of simultaneous RA attempts. The first solution is a parameter optimization scheme that adjusts the maximum number of RA attempts. The second solution

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is a resource separation scheme, in which RA resources are separated into two subsets that MTC devices can access according to the number of consecutive

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RA failures. The proposed schemes were analyzed by a mathematical model assuming a simplified operation, and a more realistic environment was considered by protocol-level simulations. From the analysis and simulation results, we can

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see that the RA retransmission has a negative impact on RA performance in an overload condition and there may be an optimal number of RA retransmissions that maximizes the RA success probability. In addition, the proposed resource separation has a performance benefit at the high RA load, whereas the original

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LTE is suitable for the light RA load.

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Acknowledgments This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2010496). This research was also supported by SK Tele-

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