Design and Performance Analysis of A Novel Distributed ...web-ext.u-aizu.ac.jp/labs/ce-cc/data/2017.11.02.Submitted-Version... · FIGURE 2: Standard LTE network access procedure with

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Digital Object Identifier 10.1109/ACCESS.2017.DOI

Design and Performance Analysis of ANovel Distributed Queue Access Protocolfor Cellular-Based Massive M2MCommunicationsANH-TUAN H. BUI1, (Student Member, IEEE), CHUYEN T. NGUYEN2, TRUONG C. THANG1, ANDANH T. PHAM1, (Senior Member, IEEE)1Computer Communications Laboratory, The University of Aizu, Japan2School of Electronic and Telecommunication, Hanoi University of Science and Technology, Vietnam

Corresponding author: Anh T. Pham (e-mail: [email protected]).

“This work was supported in part by .”

ABSTRACT This paper proposes a novel access protocol based on the Distributed Queue (DQ) mechanismto e↵ectively tackle the massive access issue in the cellular-based Machine-to-Machine (M2M) commu-nications. To fully take the advantage of the DQ mechanism, we newly propose a method to avoid theDQ’s inherent over-division problem by letting the base station first roughly probes the number of collidingdevices in a Random Access Opportunity. Based on the probing result, the base station then randomlydivides these devices into a determined number of groups and “pushes" these groups to the end of a logicalaccess queue. In addition, we develop an analytic model to accurately estimate the average access delay ofthe proposed protocol in the massive scenarios. Computer simulations are also performed to validate thecorrectness of the analytic model as well as the e↵ectiveness of the proposed protocol in comparison withthe LTE standard and conventional DQ access schemes.

INDEX TERMS Distributed queue, load estimation, LTE, massive M2M communications, random accessprotocols

I. INTRODUCTION

Machine-to-Machine (M2M) communications, which sup-ports a huge number of connected Machine-Type Devices(MTDs) operating in an autonomous manner, comes with notonly invaluable chances for life-changing smart applications[1], [2], but also serious challenges to network foundation.To best accommodate the ubiquity of MTDs, the housingcommunications infrastructure must o↵er worldwide avail-ability as well as enormous coverage. There are two mainapproaches currently being taken in an attempt to implementMTD over mobile cellular networks as depicted in Fig. 1[2]. The first choice involves organizing MTD devices ina so-called M2M area networks using short-range wirelessstandards, such as Bluetooth, wifi. These M2M area networksare then connected to a mobile Base Station (BS) via M2Mgateways equipped with Subscriber Identity Modules (SIMs).In this case, the gateways act as User Equipment (UE) frommobile network perspective. The second option is to directly

incorporate MTDs into the existing mobile networks, such asexisting LTE or the fifth generation (5G) networks [3], [4].This approach not only eliminates the need for deploymentof M2M-specific base stations, but also readily provides IP-native connectivity and thus, greatly facilitates the penetra-tion of M2M into the market. This paper tackles the designand implementation issues of the second approach, which isalso known as the cellular-based M2M communications.

One of the most challenging issues in the cellular-basedM2M communications is the massive population of MTDsand their periodic access pattern [1], which may cause theRandom Access Channel (RACH) of LTE to break downwhen thousands of devices try to access the network si-multaneously. Recent studies have pointed out that in suchscenario, the LTE RACH may experience heavy congestion,leading to very low access success probability [5]. Moreover,according to the 3GPP requirements, the cellular-based mas-sive M2M is expected to have a capacity of 30K devices per

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Bui et al.: Design and Performance Analysis of A Novel DQ Access Protocol for Cellular-Based mM2M Communications

FIGURE 1: Cellular-based M2M Communications.

cell, and for the 5G, a 10 times higher capacity, i.e. 300Kdevices per cell, should be envisioned [2]. Novel and moree↵ective access protocols are therefore the key factor for thesuccessful deployment of the M2M communications in thenext-generation mobile networks.

Over the past few years, there has been much e↵ort fo-cusing on improving the LTE standard protocols to deal withthe M2M massive access issue [8]–[12]. As a matter of fact,the 3GPP has adopted an access protocol class called AccessClass Barring (ACB) as the baseline solution to LTE RACHoverload issue. ACB scheme categorizes devices into di↵er-ent “access classes" based on various criteria, and devicesbelonging to a class may perform access request in need witha certain probability called “barring factor" [6]. Simulationresults provided in [7] with di↵erent MTD barring factorsshow that while the method does increase the access successprobability under a relatively heavy load, the access delay ofMTDs is severely degraded.

An alternative, promising solution is to employ the Dis-tributed Queue (DQ) mechanism, a near-optimum contentionresolution scheme [13]. The DQ-based protocols try to com-pletely resolve an initial access collision using an m-arysplitting tree before attempting to handle subsequent onesand thus, e↵ectively eliminates the uncertainty and instabilityseen in the LTE standard protocols [14]–[21]. Recent studiesshow that, compared to the standard ACB, the DQ-based pro-tocol exhibits noticeable improvement under a relatively highload in comparison with the LTE standard ones. Neverthe-less, the access delay is still prolonged in the massive accessscenario (more than 1,500 and 2,500 simultaneous arrivalsfor the original DQ, namely, Contention Resolution Queue(CRQ), [16] and the improved one [20], [21], respectively)due to the over-division issue.

This has motivated us to propose a novel DQ-based ran-dom access mechanism to resolve the problem of M2Mmassive access, particularly when there may be more thanten thousands simultaneous arrivals. The key concept of ourproposed protocol is to probe the number of colliding MTDsin each Random Access Opportunity (RAO) using a partialestimation strategy and randomly split these devices intoa number of groups in a way such that the over-division

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issue in DQ-based protocols can be e↵ectively avoided. Toconfirm the e↵ectiveness of the proposal, we also developan analytic model to estimate the average access delay ofthe proposed protocol over the Long-Term Evolution (LTE)networks with the massive access scenarios. The analyticresults are also validated by computer simulations, and it isseen that, compared to the baseline ACB and CRQ schemes,the proposed protocol o↵ers a significant delay reduction andreasonable average access delay in the low load and massiveaccess scenarios, respectively.

The rest of our paper is organized as follows: In section II,we briefly introduce the standard random access procedure inLTE and the conventional DQ-based protocol. Our proposedprotocol and its analytic model are presented in Section IIIand IV, respectively. Results and discussions are shown insection V and finally, section VI concludes the paper.

II. RANDOM ACCESS PROCEDURESThis section describes the overview of the standard LTE sys-tems (the ACB protocol) and the DQ-based CRQ protocol.We also discuss their limitation and layout the backgroundfor the newly proposed one.

A. LTE RANDOM ACCESS CHANNEL (RACH)Before fully examining the contention-based random accessprocedure, it is advisable to have a glimpse at the operationof the LTE RACH over which MTDs compete against eachother to send their access requests.

The LTE RACH consists of a periodic sequence of time-frequency resources explicitly allocated for random accesspurpose known as Random Access Opportunities (RAOs).The temporal periodicity of these RAOs is encoded andbroadcast to all MTDs via a parameter named PhysicalRACH (PRACH) Configufation Index. An example of thisencoding is illustrated in Fig. 3. The 3GPP specifies 64 ofsuch fixed mappings [22] where there can be a minimum of 1RAO per 2 frames to a maximum of 1 RAO per 1 subframe1.

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It should be noted that the RAOs are implemented on theuplink physical channel and thus, the BS must carefullyconsider the compromises between the amount of allocatedRAOs per frame and the amount of resources used for datatransmission.

B. STANDARD LTE PROCEDURENow let us proceed to the details of the LTE contention-basedRandom Access (RA) procedure which, as depicted in Fig. 2,is a four-message handshake between MTDs and the BS. Therole of each message is as follows.

Msg. 1, RA PREAMBLE : When an MTD needs to accessthe network, it first chooses one among a maximum of 64 or-thogonal preamble sequences as its access request and sendsthat to the BS. This transmission takes place in the nearestfuture RAO, that is, over the RACH as discussed earlier. Inthis step, multiple MTDs of the same RAO may also choosethe same preamble and cause a preamble collision that mayor may not be discovered at the BS.

Msg. 2, RANDOM ACCESS RESPONSE (RAR): Threesubframes after their preamble transmission RAO, the MTDsstart looking for a response message known as the RAR fora time window of length ra-ResponseWindowSize subframeson the Physical Downlink Shared Channel (PDSCH). Notethat the MTDs can pinpoint the location of their awaited RARinside the PDSCH based on the location information of theRAO in which their preambles were sent. The RAR contains,for each successfully detected preamble, a 6-bit “identity" ofthat preamble, timing alignment instruction and initial uplinkresource grant for Msg. 3. It may also include a Backo↵Indicator (BI) to instruct the MTDs whose used preambles’identities are not found in the RAR to backo↵ for a randomamount of time (based on BI) before attempting preambleretransmissions [23]. In the rare occasion where an MTDdoes not find any RAR during the designated time windowat all, it restarts the RA procedure.

Msg. 3, CONNECTION REQUEST : Based on the in-structions received in Msg. 2, MTDs transmit a connectionrequest message containing the reason for access request andtheir identities. Here, undetected preamble collisions fromMsg. 1 may result in more than one MTDs being grantedthe same resource to transmit Msg. 3 and cause a collision

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at the BS. In such case, colliding MTDs keep resending theirMsg. 3 without success until a certain number of attempts isreached, then restart the RA procedure [23].

Msg. 4, CONTENTION RESOLUTION : This messageis a response to MTDs whose Msg. 3 is received correctlyby the BS and contains corresponding identities. At thispoint, the MTDs whose identities are found in Msg. 4 havesuccessfully accessed the network and start scheduling theirdata transmissions, while the others restart the RA procedure.In the case where one Msg. 3 is decoded despite multipleMsg. 3 transmissions on the same time-frequency resource,only one MTD has its identity included in Msg. 4 and mayprogress [23].

Additionally, each MTD has a counter to keep track of thenumber of times it has sent a preamble. If this counter ex-ceeds a threshold denoted by preambleTransMax, the MTDshall give up on the RA procedure and indicate a randomaccess problem to upper layer. In such case, the MTD is saidto be “blocked" from accessing the network [5].

When ACB is activated, an MTD first generates a randomnumber in [0,1) and compare it with the MTD barring factorset by the BS. If the former is smaller, the UE may performRA process; otherwise it has to wait for a random durationof (0.7+ 0.6 ⇤X)⇤Barring_Time, where X is another randomnumber in [0,1), before retrying the test [6]. This mechanismhelps spreading the load across the time domain to alleviatecontention under high loads.

C. CONTENTION RESOLUTION QUEUE (CRQ) PROTOCOLIt is seen that in the LTE contention-based procedure, MTDshave to participate in an ALOHA-like contention processto transmit their preambles. At this point, the instability ofALOHA under high load has been proven [24]. Therefore,without carefully tuned barring parameters, an MTD follow-ing this mechanism may find itself continuously resendingpreambles without success due to consecutive collisions witha large number of backlogged MTDs before, eventually,

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blocked by the network.CRQ is thus proposed to tackle this issue in the LTE sup-

porting massive M2M [16]. The protocol organizes groupsof devices that select the same preambles (i.e., collidingdevices) in RAOs into a logical access “queue" based onthe relative order of colliding preambles themselves. Thatis, when preamble collisions occur in an RAO, the groupof MTDs selecting the lower-in-order colliding preamble is“pushed" to the end of the queue in advance of those whopick the higher-in-order one. Then in each RAO, given thatthe queue is non-empty, only the group of MTDs residing atthe head of the queue may exit and re-perform the preambletransmission step. This splitting tree-like mechanism pre-vents MTDs involved in a collision from intervening in futureretransmissions of MTDs involved in other collisions. Asa result, the CRQ can eliminate the uncertainty factor tosupport an unlimited number of simultaneous arrivals.

An example of CRQ protocol with three available pream-bles is displayed in Fig. 4. In the first RAO, all devicescollide and enter the queue. (u1, u7) occupy the first positionsince they used the lowest-in-order colliding preamble while(u3, u4) and (u2, u5, u6) take up the second and third place,respectively. The two first groups then succeed in RAO 2 and3 and exit the queue. The third group (u2, u5, u6) retransmitsin RAO 4, but only u6 makes it to leave while the otherscollides and re-enters the queue. Finally, (u2, u5) leave thequeue in RAO 5 where they succeed.

It is seen from Fig. 4 that each time a preamble collisionoccurs in an RAO, the CRQ, in essence, reserves a futureRAO for the group of involved MTDs. This mechanismmay cause excessive RAOs reservation when there are manypreamble collisions occurring in an RAO, but each collisioninvolves relatively few MTDs. That is, under such scenario,the CRQ inadvertently reserves a large number of RAOs(equal to the number of preamble collisions), but each RAOonly serves an amount of involving MTDs way lower thanits capability. This phenomenon, hereby referred to as over-division, is the main culprit behind the CRQ’s performanceinconsistency as proved in our earlier work [21]. To bettervisualize the impact of over-division, let us assume that thereare 90 MTDs and 18 preambles to start with. Also let usassume that in the first RAO, each preamble is selectedby 5 MTDs and ends up in a collision. Since there are 18preamble collisions in total, the CRQ books 18 future RAOs(i.e., RAO 2 to RAO 19). Retransmitting in each of theseRAOs, however, are only 5 MTDs, which are far less thanthe number of usable preambles i.e., 18. As a consequence, ahuge number of unused preambles is generated and the delayis severely prolonged.

III. PROPOSED ACCESS PROTOCOLAs it is described in the last Section, that each of currentapproach has its own pros and cons in the considered contexti.e., the massive M2M access. While the ACB scheme maywork reasonably well in such case, it does so at the cost of acertain degree of instability which may result in excessive

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access delay of a small number of devices. Furthermore,improper setting of barring parameters may cause RACHoverload when the number of devices exceed a certain mark.On the contrary, the CRQ protocol can theoretically supportan unlimited number of devices [5]. Nevertheless, its over-division issue may cause excessive access delay for thewhole population in massive multiple access scenario, asproved in our previous work [21]. This has motivated us topropose a novel design of DQ-based protocol that enjoysthe advantages of both approaches to resolve the massivemultiple access dilemma in an e�cient manner.

The key concept of our protocol is to probe the numberof colliding MTDs in each RAO using a partial estimationstrategy and randomly split these devices into a number ofgroups in a way such that over-division can be avoided. Infollowing sections, we describe the load estimation methodsand the details of the proposed protocol.

A. LOAD ESTIMATION METHODSLoad estimation plays a crucial role in reducing randomaccess delay, as it facilitates the design of optimal schedul-ing schemes. In fact, various estimation methods have beenproposed to optimize the performance of random accessprotocols, albeit mainly in Radio Frequency IDentification(RFID) literature [25]. Recently, similar e↵orts have beenmade for cellular-based M2M with fixed-location devices(which is also our considered case in this paper). Recent workconfirms that it is feasible to estimate the number of suchdevices in an RAO with a relatively good accuracy as long asthey can maintain the same received power at the BS (which,practically, can be done via power control schemes) [26].

Particularly, the method in [26] divides the cell coverageinto Nt regions, where devices in the same region havesame delay to the BS. Then, each time transmissions fromdevice(s) selecting an i-th preamble in the j-th spatial re-gion are detected, the corresponding delayed version of thepreamble i.e., Pi, j is subtracted from the received signal Y .This process cycles between all preambles until the powerof Y drops below a threshold, and the number of devices

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selecting a particular preamble is found by counting thenumber of subtractions that have been made correspondingto that preamble.

The major issue with such iterative estimation method isthat the needed number of iterations is roughly the sameas the number of transmitting devices in the RAO. Thisleads to an excessive overhead delay, which may becomeprohibitive in massive access scenario with tens of thousandsof simultaneous arrivals. We therefore propose an additionalpartial estimation step where we first probe the power levelP of a randomly chosen i-th preamble by correlating it withthe received signal Y as follows

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where T is the duration of an OFDM symbol, Pi is the powerfrom devices using the i-th preamble, and Pn is the noisepower out of the integration-and-summation unit. To someextent, P can be used as a representative for the total numberof transmitting devices. Thus, we may infer that if P is higherthan a certain threshold �, then the total number of transmit-ting devices is also likely to be more than a corresponding N�.In such case, we only perform this partial estimation step,instead of the iterative estimation method as proposed in [26],in order to guarantee system response time. We will onlyperform the iterative estimation when the partial estimationstep infers that the number of transmitting devices is less thanN�. An example of this strategy is illustrated in Fig. 5 wherethe probed preamble is randomly chosen to be the first one.

It is noteworthy to mention that � can be obtained viatraining for di↵erent values of N� and can be set at the BS.In this paper, we assume that � is chosen corresponding toN� = K2, where K is the number of available preambles. Inother words, our key assumption is that whenever the numberof transmitting MTDs in an RAO is higher than K2, the BSwill not necessarily perform the iterative estimation and thus,will not know the total number of accessing MTDs.

B. DQ-BASED ACCESS PROTOCOL WITH LOADESTIMATIONNow, we present our proposed DQ-based access protocolusing load estimation methods to handle collisions undermassive simultaneous access scenario. The key point of ourprotocol is that it controls the number of groups, G, whichunsuccessful MTDs are divided into, so that the over-divisionissue can be e↵ectively avoided. In particular, the BS firstprobes the number of transmitting MTDs i.e., Nr, in an RAOusing our partial estimation strategy described earlier andselect G as follows. Note that Ns here denotes the number ofsuccessful devices (or equivalently, the number of preamblesselected by only one device) in an RAO.• If Nr > K2, then the number of devices are high enough

so that the BS assumes Ns = 0. In this case, G = K.This does not cause over-division since the number ofunsuccessful MTDs per group is still more than K.

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where [·] denotes the round operator, so that the numberof unsuccessful MTDs per group is K on average to pre-vent over-division. Furthermore, when (Nr �Ns) < K,the number of unsuccessful MTDs in this RAO is smallenough that further division is always excessive. In suchcase, no more division is needed and thus, G = 1.

These G groups are then “pushed" to the end of a “dis-tributed queue" to retry later. It is important to note thatunsuccessful devices are not divided to groups based on theirused preambles as in the CRQ, but rather in a random mannerinto a predetermined number of groups i.e., G.

As stated above, to realize the protocol, it is necessaryto maintain a logical “distributed queue" wherein collidingdevices are organized. This queue does not exist physically,but is facilitated via two parameters named DQ and pDQ.The former represents current “length" of the queue and ismaintained by the BS while the latter is stored exclusively ateach device to inform the device about its current “position"inside the queue. A device may transmit if its pDQ = 0 i.e., ifit is at the queue’s head. These parameters encode all relevantinformation on the queue and are updated after each RAOaccording to the following rules.

For DQ (at the BS):• If DQ > 0, then DQ is decreased by 1 due to removal of

the entry at the head of the queue.• If preamble collisions occur, DQ = DQ+G to reflect the

addition of G groups of MTDs into the queue.For pDQ (at individual MTD):• If the device is waiting in the queue i.e., pDQ > 0,

then pDQ = pDQ�1 due to removal of the entry at thequeue’s head.

• If the device collides with others of the same preamble,it randomly select an integer g between [0, G - 1] toreflect its choice of group. Then its pDQ = DQ�G+ gto indicate that it, along with other devices of the g-thgroup, has re-entered the queue from the end. Note thatG and DQ are included in corresponding Msg. 2.

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Newly arrived MTDs must first observe the status of theongoing process by listening to corresponding Msg. 2. Ifthere are unsolved collisions (DQ > 0), they wait for DQRAOs before re-checking Msg. 2 to acquire an updated valueof DQ. This procedure is repeated until they find a Msg. 2indicating that DQ = 0. In such case, the devices may invokethe access procedure in the very next RAO. Compared to theCRQ, these settings not only prevents newly arrived devicesfrom interfering in the ongoing process, but also lower theenergy consumption of such devices as they do not need tolisten to Msg. 2 continuously.

Figure 6 presents an example of our protocol with K = 6available preambles. Here, each rectangular represents anRAO while the upper and lower numbers in a rectangularrepresent the number of transmitting devices Nr and thenumber of unsuccessful devices i.e., Nr � Ns, in that RAO,respectively. In the very first RAO, Nr = 90 devices transmitsimultaneously and cause collisions in all preambles. Viapartial estimation step, the BS knows that Nr > K2 in thisRAO. Thus, it safely sets G = K = 6 to randomly break these90 MTDs into 6 groups without triggering over-division. Thatis, the BS does so without worrying about the number ofMTDs per group falling very short compared to K. The 6groups then exit the queue in turn to re-perform preambletransmissions in the next 6 RAOs i.e., RAO 2 to RAO 7.Again, via partial estimation step, the BS is aware that thenumbers of transmitting MTDs i.e., Nr, in each of theseRAOs are lower than K2. Thus, the BS first performs itera-tive estimation to obtain accurate Nr and Ns of each RAO.Then, to avoid over-division, the BS instructs the Nr � Nsunsuccessful MTDs in each of these RAOs to split intoG =

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(which are well around K) in each. All 13 groups createdduring RAO 2 to RAO 7 then consecutively exit the queue toretransmit in RAO 8 to RAO 20. As the number of MTDs ineach of these 13 groups is approximately K, RAO 8 to RAO20 achieve maximum e�ciency (which occurs when Nr ⇡ K).The number of unsuccessful MTDs per RAO from that pointalso drops below K and thus, no further division is requiredand G is always 1 afterwards.

C. MULTIPLE DISTRIBUTED QUEUES AND PROPOSEDACCESS PROCEDUREIn the previous subsection, we have comprehensively de-scribed the operation of our proposed protocol which, how-ever, only acts as a conceptual replacement to the underlyingALOHA-like framework of LTE. To be considered practical,the protocol must fully satisfy the timing requirements of theLTE’s handshake procedure. Such task is actually non-trivialdue to the reasons discussed in the following paragraph.

From Fig. 2, it is seen that after performing a pream-ble transmission, an MTD cannot take any further actionsuntil the corresponding RAR-probing time window is over.

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This time window begins at exactly three subframes afterthe MTD’s preamble transmission RAO and has a lengthof ra-ResponseWindowSize subframes, which results in atotal locking interval of W = 3 + ra-ResponseWindowSizesubframes. Consequently, any RAOs taking place during thisinterval is considered unusable for the transmitting MTD.This barely a↵ects the ACB scheme, as RAOs unusable fora transmitting MTD may still be utilized by others who justresume from their previous backo↵ or barring wait. On thecontrary, DQ-based protocols require that all MTDs (thatis, both transmitting and waiting ones) do not adjust theircounters until transmitting MTDs have received their RARi.e., until the locking interval W of transmitting MTDs haselapsed, to ensure that the queue operates in a synchronousmanner. Thus, in e↵ect, a single distributed queue can onlyuse RAOs that are at least W subframes apart from each other.This leads to under-utilization when the RAOs are spaced atless than or equal to W subframes.

To overcome this issue, we propose to facilitate morethan one distributed queues running in parallel so that RAOsunusable by a queue will be exploited by the others. Our ideais best described via the example provided in Fig. 7 wherera-ResponseWindowSize is set to 5 subframes [27], and theRAOs are located at subframes number 1 and 6 of any frames(which is equivalent to setting PRACH Configuration Indexto 6). If there is only one queue, it is easy to see that to satisfythe RAOs spacing condition i.e., consecutive usable RAOsof a queue must be spaced at least W subframes apart, thequeue can only use either the pair of subframes number 1or 6, which leads to only 50% RAOs utilization. To achievefull RAOs utilization while still secure the RAOs spacingconstraint, we propose the parallel use of two separate queueswhere the first occupies the pair of subframes number 1 andthe second takes the pair of subframes number 6 as depictedin the middle and bottom of Fig. 7, respectively. This conceptextends naturally to other PRACH configurations. Also, weassume that the number of parallel queues as well as theallocated positions of their RAOs inside a frame are broadcastto all MTDs by the BS as part of system information. Uponentering the network, an MTD will randomly select a queue

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:DLW�DQi5$2V�RI�i�WK

TXHXH

2EWDLQ�DQi LQ�FRUUHVSRQGLQJ�

0VJ��

initiation

n y

y

n

:DLW�IRU�QH[W�5$2�RI�i�WK TXHXH

W

VXEIUDPHV

FIGURE 8: Access procedure representation of the proposal.

d ��

d ��

d с�da

*�� .

*�� .

*�da � ��

ǡ

*�da��

d �da � �

FIGURE 9: Analytic delay model of the proposed protocol.

to associate with.The access procedure, modified to accommodate our pro-

posed protocol, can finally be visualized as in Fig. 8.

IV. ANALYTIC MODEL & PERFORMANCE ANALYSISIn this section, we propose an analytic model to study theaccess delay of our proposed protocol. The operation of theproposed protocol can be visualized as an m-ary splitting treein Fig. 9, where each node corresponds to an RAO, and thenumber of children of a node corresponds to the number ofgroups which unsuccessful devices in that node are dividedinto. Here, we perform approximation by assuming that everynodes of the same depth d has the same number of childrendenoted by G(d), and the number of devices in each childnode is also the same. G(d) can be written as

G(d)=

8>>>>>>><>>>>>>>:

max

Nr(d)�Ns(d)K

�,1

!, Nr(d) K2

K , Nr(d) > K2

(2)

where Nr(d) and Ns(d) is the average number of transmittingdevices and successful devices in a node of depth d. Ns(d)is related to Nr(d) as Ns(d) = Nr(d)Ps(d), where Ps(d) is theprobability of success of a device given that it is at a node ofdepth d. Ps(d) may be interpreted as the probability that noother devices in the node selects the same preamble as thisdevice and thus, is expressed as

Ps(d) = 1� 1

K

!Nr(d)�1

. (3)

As seen from the Fig. 9, if an MTD succeeds at a depthd, it will have waited for a number of RAOs equal the totalnumber of nodes from all previous depths plus, on average,half of the number of nodes of d (as it may belong to any nodeof d with equal probabilities). For convenience, we denotethe number of nodes of a depth d by Ln(d) and the averagenumber of elapsed RAOs for a device succeeding at d byL(d). The relationship between the two is expressed as

L(d) =d�1X

d0=1

Ln(d0)+Ln(d)

2. (4)

In order to find Ln(d), we must know G(d0) for all d0 <d as they directly a↵ect the number of nodes at each depthupto d. It is evident that G(d0) = K for all depths d0 at whichNr(d0) > K2. On the other hand, at a particular depth (da�1),the number of devices per node start dropping below K2 andthus,

G(da�1) =max

0BBBBBB@

2666664�1�Ps(da�1)

�Nr(da�1)

K

3777775 ,1

1CCCCCCA . (5)

This makes the average number of devices per node at thevery next depth Nr(da) approximately K and thus, no moredivision is needed afterwards i.e., G(d0) = 1 for all d0 � da.Having found all G(d) based on da, we then proceed tocalculate Ln(d) as follows. Note that it is trivial that Ln(1)= 1.

VOLUME 4, 2016 7


• For 2 d (da�1):

Ln(d) =d�1Y

d0=1

G(d0) = Kd�1. (6)

• For d = da, Ln(d) is rewritten as

Ln(da) =G(da�1)da�2Y

d0=1

G(d0) =G(da�1)Kda�2. (7)

• For d > da, there is no further splitting and thus,

Ln(d) = Ln(da) =G(da�1)Kda�2. (8)

It is clear that da must be derived for specific calculation ofL(d). To find da, we first observe that Ps(d) ⇡ 0 and G(d) = Kfor all d < da�1. Then, Nr(da�1) can be found as

Nr(da�1) =N

Kda�2 . (9)

It is also reminded that at da � 1, the number of devicesper node starts dropping below K2 i.e., Nr(da � 1) < K2.Thus, N < Kda and since da is the smallest integer thatsatisfies this condition, we can conclude that da =

l log(N)log(K)

m. It

is important to note that G(da�1) is consequently derived bysubstituting Nr(da�1) in (9) into (5) and (3). This completesthe calculation for L(d).

Now that L(d) is found, the remaining task is to derive theprobability that an MTD succeeds at d denoted by Pr(d).This is interpreted as the probability that the device fails atall depths prior to d and then succeeds at d, which is thenexpressed as

Pr(d) = Ps(d)d�1Y

d0=1

⇣1�Ps(d0)

⌘. (10)

As seen from (3), Nr(d) is required to calculate Ps(d) for anyd. In general, Nr(d) can be written as

Nr(d) =�1�Ps(d�1)

�Nr(d�1)

G(d�1). (11)

Therefore, Nr(d) is a function of Nr(d�1). This establishesa recursive solution to find Nr(d) for any d, where the initialcondition is Nr(1) = Nq where Nq denotes the total numberof simultaneous arrivals at the MTD’s choosen queue. Thus,Pr(d) for any d is consequently derived. Finally, the averagenumber of RAOs (or correspondingly, the average accessdelay) that an MTD has to wait, denoted by D(Nq), is foundas

D(Nq) =1X

d=1

L(d)Pr(d). (12)

Note that D(Nq) is measured in the unit of RAOs of theMTD’s chosen queue. That is, these RAOs must be spacedat least W subframes apart of each other. To provide a morepractical result, we fix the value of W to 8 subframes as inFig. 7 and the spacing between consecutive RAOs of a queueto 9 subframes (which is well longer than W) so that thereis one RAO per one frame i.e., one RAO every 10ms, for

every queue. The average delay that an MTD has to wait untilits successful preamble transmission, measured in the unit ofseconds, is then easily found as 0.01⇤D(Nq)

Finally, let us assume that there are N simultaneous arrivalsto the system and Q queue(s) running in parallel. Since theMTDs randomly choose one among Q queue(s) to performaccess, each queue serves on average Nq = N/Q simultaneousarrivals. Furthermore, since the queues run in parallel, theaverage delay of an MTD of any queue is also the averagedelay of all MTDs. That is, the average delay that an MTDhas to wait until its successful preamble transmission givenN and Q, measured in the unit of seconds, is written as 0.01⇤D(N/Q)

V. RESULTS & DISCUSSIONSIn this section, computer simulations using basic MATLABprogramming are performed to validate our analytic modelfor delay analysis, as well as to demonstrate the e↵ectivenessof the proposed protocol in comparison with standard ACBscheme and the CRQ protocol. Configuration parametersfor the RA procedure are summarized in Table 1 and areassumed to be known by all MTDs. As for the tra�c model,we employ batch arrivals with the following assumptions(which are reasonable for M2M tra�c):• N MTDs try to access the network simultaneously every

Tp seconds, where Tp is the period of the batches.• There is no new arrivals between two consecutive

batches.• Tp is long enough that all collisions from previous batch

has been resolved by the time a new batch arrive.The protocols of interest are then assessed using access

delay as the main performance metric. Note that we onlyobserve the random access delay of an MTD until when itsuccessfully sends its preamble, as following Msg. 2, 3 and 4transmissions are not random but scheduled by the BS. Theaccess delay of the MTD, in the unit of seconds, is then foundas 0.01 ⇥ the number of RAOs of the chosen queue that haselapsed until the MTD’s successful preamble transmission.It is also worth mentioning that the average access delay iscalculated over non-blocked MTDs only. This means that thedelay of blocked MTDs i.e., MTDs who have exceeded themaximum allowed number of preamble retransmissions, isnot considered.

We first validate the analytic delay model by the computersimulation in Fig. 10 that shows both the analytic and ob-tained simulation results of the average access delay of theproposed protocol with the number of arrivals, N, of up to10,000 and PRACH Configuration Index set to 3. It is seenthat there is a good agreement between the two in all cases,which validates the correctness of the analytic model. Usingthis model, ones can quickly estimate the average accessdelay of MTDs with a specific system configuration.

Next, we confirm the e↵ectiveness of the proposed proto-col, in terms of average access delay, in comparison with theLTE standard scheme (ACB) and the conventional DQ-basedCRQ protocol under the settings of PRACH Configuration

8 VOLUME 4, 2016


TABLE 1: Simulation Parameters

Parameter Simulated ValuesNumber of available preambles K (associated barring factor) 56 (0.8) , 36 (0.6) , 18 (0.4)Barring time Tbar 1s , 1.5s , 2sBacko↵ indicator 480msra-ResponseWindowSize (resultant locking interval W) 5 subframes (8 subframes)Spacing between consecutive RAOs of a queue 9 subframesPRACH Configuration Index (resultant number of queues Q) 3 (1) , 6 (2) , 9 (3)Maximum allowed number of preamble retransmissions preambleTransMax 10

0 2000 4000 6000 8000 10000

Number of Simultaneous Arrivals, N

0

1

2

3

4

5

6

7

8

9

Avera

ge A

ccess D

ela

y (

s)

Theoretical, K = 18

Theoretical, K = 36

Theoretical, K = 56

Simulation

FIGURE 10: Analytical vs. simulation result of averageaccess delay of the proposed protocol, given PRACH Conf.Index = 3.

Index = 3 and Tbar = 2 seconds. Initially, in Fig. 11a wecompare all three protocols in the load range of up to 2,100simultaneous arrivals. The result clearly shows that our pro-posed protocol o↵ers a significant improvement compared toboth ACB and CRQ at any level of load. As an example, at thenumber of arrivals N = 1500, the advantages of ours over theCRQ are seen to be 37%, 65% and 54% when there are 18, 36and 56 preambles, respectively. Compared to the ACB underthe same conditions, these numbers respectively become71%, 75% and 68%. The result also confirms the fact, asalso can be seen in [16], that the DQ-based protocol (CRQ)outperforms the ACB in this range of load. Nevertheless,as the load gets higher, the CRQ su↵ers from higher accessdelay than that of the ACB due to excessive reservation issuewhich becomes apparent. It is shown that, at N = 1,900, theACB starts outperforming the CRQ. Thus, for better clarity,we omit the CRQ and use the ACB as reference in massivesimultaneous arrivals scenario.

Delay performance of our protocol and the ACB schemein the high load region is found in Fig. 11b where weassess up to around 15,000 simultaneous arrivals. It is seenthat the proposal still shows a remarkable gain in the rangeof 1,900 N 5,000. However, as the system enters themassive multiple access region of N > 5000, the performancegap between the protocols becomes gradually narrower. This

trend continues until the ACB scheme reaches a breakpointat which its average delay is dangerously close to ours. Nev-ertheless, the average access delay of ACB starts to increaserapidly beyond these marks, which suggests that its RACHhas been overloaded. On the contrary, our protocol still keepsa stable delay performance to re-widen the advantage gap. Asan example, at N = 14,700 and K = 56, this gap has alreadybeen re-expanded to 16%.

We also note that the breakpoints of ACB for di↵erent Kare di↵erent, where K = 18, 36 and 56 show breakpointsat N = 13,100, 11,500 and 9,900 respectively. This mayseem odd at first since the cases with higher K reach theirbreakpoints sooner. However, it is important to keep in mindthat the barring factor is much smaller when K is low, whichgreatly disperses access attempts on the time domain. In suchcase, the RACH can accommodate more MTDs at the cost ofsignificantly higher access delay.

Figure 12 is then plotted in an attempt to investigate theaccess delay of the system under di↵erent LTE settings atthe peak load of the considered massive access region, i.e.N = 14,700. Here, the immediately recognized feature is thateven with di↵erent LTE configurations, our protocol managesto surpass the ACB scheme in most cases. Furthermore, ob-served access delay of the latter appears to vary in an arbitrarymanner. To better understand such dynamic, it is recalled thatthe access delay is averaged over non-blocked MTDs only.Thus, the access delay should be viewed in conjunction withthe blocking probability, defined as the ratio of the number ofblocked MTDs to the total number of MTDs N and used as anindicator for the severity of access congestion, for a completepicture.

The blocking probabilities corresponding to Fig. 12 isdisplayed in Table 2 2. For the sake of discussion, we hence-forth consider the system (using ACB) as in non-overloadedstate if the blocking probability is approximately 0%, andin overloaded state otherwise. It is consequently seen thatwhenever the system is non-overloaded e.g., in most of thecases associated with PRACH Configuration Index = 6 or9, ACB scheme at lower Tbar performs very similar to theproposal. As Tbar grows, however, access delay of the formeris prolonged, causing it to fall short against our protocol.This is because being in non-overloaded state implies thatthe system with ACB is capable of handling the o↵eredload, and increasing Tbar thus unnecessarily defers MTDs’

2Our protocol does not have blocking under the simulation conditions inTable 1 and thus, is dropped from this table

VOLUME 4, 2016 9


300 700 1100 1500 2100


0

1

2

3

4

5

Av

era

ge

Ac

ce

ss

De

lay

(s

)

Proposed protocol, K = 18



ACB scheme, K = 18

ACB scheme, K = 36

ACB scheme, K = 56

CRQ protocol, K = 18



(a) Lower access region

2000 4000 6000 10000 14000


0

5

10

15

Av

era

ge

Ac

ce

ss

De

lay

(s

)




ACB scheme, K = 18

ACB scheme, K = 36

ACB scheme, K = 56

(b) Massive access region

FIGURE 11: Average access delay comparison of the protocols, given PRACH Conf. Index = 3 and Tbar = 2s.

.� ��

� � �35$&+�&RQILJXUDWLRQ�,QGH[

�

�

�

�

�

��

��

��

��

��

$YHUDJH�$FFHVV�'HOD\��V�

.� ��


�

�

�

�

�

�

�

�

�

�


3URSRVDO $&%� Tbar

= 1s $&%� Tbar

= 1.5s $&%� Tbar

= 2s

.� ��


�

�

�

�

�

�

�


FIGURE 12: Average access delay of the proposal and ACB under peak load i.e., N = 14700.

(re)transmissions without significantly improving the alreadyinfinitesimal blocking probability.

When the system is overloaded, e.g. PRACH Configura-tion Index is 3, our proposed protocol outpaces the ACBscheme by a remarkable margin for all tested Tbar. Moreover,we can safely assure that although access delay of the latterseems to gradually drops as the barring time increases, itcannot drop below ours even if Tbar keeps getting prolonged.This claim is backed up by the fact that at Tbar settings wherethe observable performance gap is smallest i.e., at Tbar = 2s,the blocking probabilities of the ACB is already very close to0%. That is, further increment in Tbar quickly puts the systemback to the non-overload state in which the delay is boundedto rise as Tbar gets higher.

Finally, one may conclude from Fig. 12 that in the case

of very high load, i.e. massive arrivals from MTDs, ourprotocol shows a performance level that is anywhere fromcomparable with to significantly better than the ACB. Whatwe want to emphasize here is that the ACB scheme requiresalmost perfect coordination between all LTE parameters inaccordance with a specific load to achieve the performancelevel similar to what ours has to o↵er.

VI. CONCLUSIONS & FUTURE WORKIn this paper, the foreseen massive multiple access issue inthe context of cellular-based M2M communications had beenthroughout studied. It was seen that in dense scenario wherehundreds of thousands of MTDs are expected to be housedby a single base station, simultaneous access attempts froma measly 1% of the population could still impose significant

10 VOLUME 4, 2016


TABLE 2: Blocking probability of ACB schem under peak load i.e., N = 14700.

(a) K = 18

Tbar

Conf.Index 3 6 9

1 second 46.67% 0.34% 0%1.5 seconds 10.49% 0.01% 0%2 seconds 1.07% 0% 0%

(b) K = 36

Tbar

Conf.Index 3 6 9

1 second 41.98% 0.26% 0%1.5 seconds 10.22% 0.01% 0%2 seconds 1.39% 0% 0%

(c) K = 56

Tbar

Conf.Index 3 6 9

1 second 56.47% 2.15% 0.02%1.5 seconds 34.55% 0.3% 0%2 seconds 16.38% 0.05% 0%

stress on the current-gen LTE networks’ random access chan-nel. Several existing countermeasures, including both the3GPP’s ACB scheme and the CRQ protocol, as well as theirpros and cons, were also investigated. More importantly, wethen introduced a novel DQ-based random access protocolcoupled with an estimation strategy to address the issue in amore e�cient manner. By randomly dividing unsuccessfulMTDs in an RAO into a number of groups based on theestimation strategy’s output and pushing these groups to theend of a logical access queue to retry later, our protocol wasable to avoid both the over-division problem of the CRQ andthe uncertainty factor of the ACB. The use of multiple accessqueues running in parallel was also suggested to fully exploitthe closely-spaced RAOs. Finally, we constructed a delaymodel for the proposal and performed computer simulationsto validate both the analytic model and the e↵ectiveness ofthe proposed protocol over the ACB and the CRQ.

The simulation results showed that the CRQ initiallyoutperformed the ACB scheme, but quickly lost its favorwhen the load increased due to over-division. Our protocol,however, outpaced both the ACB and CRQ in a wide rangeof load up to around 15,000 simultaneous access attempts,given a certain LTE configuration. Under closer inspection atpeak load, it was seen that even with di↵erent LTE config-urations, our protocol exhibited a performance level rangingfrom comparable with to remarkably better than the ACB.Thus, the proposed protocol emerged as one highly feasiblesolution for massive M2M access scenario, when the CRQsu↵ered from the severe impact of over-division and the ACBstarted getting overloaded.

Finally, it is worth noting that besides the simultaneousaccess pattern considered in this paper, there exists otherapproaches to model MTDs’ access patterns as well. In fact,the 3GPP has readily provided two references, quoted as theuniform and Beta tra�c models, to depict the access patternsof uncorrelated and highly correlated MTDs, respectively.These models assume that the MTDs wake up to performrandom access following certain temporal distributions over afixed period, which is significantly di↵erent from the studiedsimultaneous case where the MTDs access at the same timeinstance. Therefore, in our future works, we are interested inseeing how our protocol fares compared to the ACB and othernovel access schemes under the 3GPP’s reference setups.

REFERENCES[1] D. Boswarthick et al., “M2M Communications : A System Approach,"

Wiley & Sons, 2012.[2] M. Dohler et al., “5G Mobile and Wireless Communications Technology"

Cambridge University Press, 2016.[3] 3GPP TR 22.868 V8.0.0, “Study on Facilitating Machine to Machine

Communication in 3GPP Systems," Mar. 2007.[4] ETSI TS 102.690 V2.1.1, “Machine to Machine communications (M2M);

Functional architecture," Oct. 2013.[5] A. Laya et al., “Is the Random Access Channel of LTE and LTE-A Suitable

for M2M Communications? A Survey of Alternatives," Commun. SurveysTuts., vol. 16, no. 1, pp. 4-16, Dec. 2013.

[6] 3GPP TS 36.331 V10.5.0, “Evolved Universal Terrestrial Radio Access(E-UTRA); Radio Resource Control (RRC)," Mar. 2012.

[7] 3GPP TSG RAN WG2 # 71 R2-104662, “MTC simulation results withspecific solutions," Commun. Surveys Tuts., Aug. 2010.

[8] P. Si et al., “Adaptive Massive Access Management for QoS Guaranteesin M2M Communications," IEEE Trans. Veh. Technol., vol. 64, no. 7, pp.3152-3166, July 2015.

[9] A. Ilori et al., “A random channel access scheme for massive machinedevices in LTE cellular networks," Proc. IEEE ICC, London, 2015, pp.2985-2990.

[10] S. Duan et al.,, “D-ACB: Adaptive Congestion Control Algorithm forBursty M2M Tra�c in LTE Networks," IEEE Trans. Veh. Technol., vol.65, no. 12, pp. 9847-9861, Dec. 2016.

[11] G. Y. Lin et al., “Estimation and Adaptation for Bursty LTE RandomAccess," IEEE Trans. Veh. Technol., vol. 65, no. 4, pp. 2560-2577, April2016.

[12] J. Misic et al., “Sharing It My Way: E�cient M2M Access in LTE/LTE-ANetworks," IEEE Trans. Veh. Technol., vol. 66, no. 1, pp. 696-709, Jan.2017.

[13] W. Xu and G. Campbell, “A Near Perfect Stable Random Access Protocolfor a Broadcast Channel," Proc. IEEE ICC, June 1992.

[14] F. Vï£¡zquez-Gallego et al., “Energy performance of distributed queuingaccess in Machine-to-Machine networks with idle-to-saturation transi-tions," Proc. IEEE GLOBECOM Wkshps, Atlanta, GA, 2013, pp. 867-872.

[15] F. Vï£¡zquez-Gallego et al., “Energy analysis of a contention tree-basedaccess protocol for machine-to-machine networks with idle-to-saturationtra�c transitions," Proc. IEEE ICC 2014, Sydney, NSW, 2014, pp. 1094-1099.

[16] A. Laya et al., “Contention Resolution Queues for Massive Machine TypeCommunications in LTE," IEEE PIMRC Workshop on M2M Communica-tions: Challenges, Solutions and Applications, pp. 2314-2318, Sep. 2015.

[17] A. Laya et al., “E�cient Contention Resolution in Highly Dense LTENetworks for Machine Type Communications," IEEE Global Communi-cations Conference (GLOBECOM), pp. 1-7, Dec. 2015.

[18] F. Vazquez-Gallego et al., “Energy Harvesting-Aware Distributed Queu-ing Access for Wireless Machine-to-Machine Networks," Proc. IEEEGLOBECOM, Washington, DC, 2016, pp. 1-7.

[19] F. M. Awuor and C. Y. Wang, “Massive machine type communication incellular system: A distributed queue approach," Proc. IEEE ICC, KualaLumpur, 2016, pp. 1-7.

[20] Anh-Tuan H. Bui and Anh T. Pham. “E�cient Access Protocol for MassiveM2M Communications in LTE". IEICE General Conference 2017, pp. BS-1-34, Meijo University, Nagoya. Mar. 2017.

[21] Anh-Tuan H. Bui, Chuyen T. Nguyen, Truong C. Thang and Anh T.Pham. “An Improved DQ Access Protocol for Cellular-based MassiveM2M Communications". IEEE/CIC ICC China 2017. Qingdao, China,Oct. 2017.

[22] 3GPP TS 36.211 V10.4.0, “Evolved Universal Terrestrial Radio Access(E-UTRA); Physical Channels and Modulation,âAI Dec. 2011.

VOLUME 4, 2016 11


[23] 3GPP TS 36.321 V9.3.0, “Evolved Universal Terrestrial Radio Access (E-UTRA); Medium Access Control (MAC)," Jun. 2010.

[24] S. Ghez et al., “Stability Properties of Slotted ALOHA with MultipacketReception Capability," IEEE Trans. Autom. Control, vol. 33, no. 7, pp.640-649, Jul. 1988.

[25] A. Laya et al., “Goodbye, ALOHA!," IEEE Open Access, pp. 2029-2044,Apr. 2016.

[26] M. Shirvanimoghaddam et al., “Massive Multiple Access Based on Super-position Raptor Codes for Cellular M2M Communications," IEEE Trans.Wireless Commun., vol. 16, no. 1, pp. 307-319, Jan. 2017.

[27] C. A.-Haro and M. Dohler, “Machine-to-Machine (M2M) Communica-tions : Architecture, Performance and Applications," Woodhead Publish-ing, 2015.

Anh-Tuan H. Bui (SM’17) received the B.E. de-gree in communications engineering from HanoiUniversity of Science and Technology, Vietnam,in 2016 and is currently pursuing a M.E. degree atthe University of Aizu, Japan. His study in Japan isfunded by the Japanese Government Scholarship(MonbuKagakusho). His research interests are inthe field of protocol designs, system modeling andperformance evaluation for next generation (5G)wireless networks.

Chuyen T. Nguyen received the M.S. degreein communications engineering from NationalTsing-Hua University, Taiwan, in 2008, and thePh.D. degree in informatics from Kyoto Univer-sity, Japan, in 2013. In 2014, he was a Visiting Re-searcher with The University of Aizu, Japan. He iscurrently an Assistant Professor with the School ofElectronics and Telecommunications, Hanoi Uni-versity of Science and Technology, Vietnam. Hisresearch interests include statistics, optimization

algorithms and their applications in wireless communication systems. Hereceived the Fellow award from the Hitachi Global Foundation in 2016.

Truong C. Thang received the B.E. degree fromHanoi University of Science and Technology,Vietnam, in 1997 and the Ph.D. degree fromKAIST, Korea, in 2006. From 1997 to 2000, heworked as a network engineer in Vietnam Post& Telecom (VNPT). From 2007 to 2011, he wasa Member of Research Sta↵ at Electronics andTelecommunications Research Institute (ETRI),Korea. He was also an active member of Koreanand Japanese delegations to standard meetings of

ISO/IEC and ITU-T from 2002 to 2014. Since 2011, he has been anAssociate Professor of University of Aizu, Japan. His research interestsinclude multimedia networking, image/video processing, content adaptation,IPTV, and MPEG/ITU standards.

Anh T. Pham (M’00–SM’11) received the B.E.and M.E. degrees in electronics engineering fromthe Hanoi University of Technology, Vietnam, in1997 and 2000, respectively, and the Ph.D. degreein information and mathematical sciences fromSaitama University, Japan, in 2005. From 1998to 2002, he was with NTT Corporation, Vietnam.Since 2005, he has been a Faculty Member withThe University of Aizu, where he is currently aProfessor and the Head of the Computer Commu-

nications Laboratory, Division of Computer Engineering. His research inter-ests are in the broad areas of communication theory and networking witha particular emphasis on modeling, design, and performance evaluation ofwired/wireless communication systems and networks. He has authored/co-authored over 160 peer-reviewed papers on these topics. Dr. Pham is seniormember of IEEE. He is also member of IEICE and OSA.

12 VOLUME 4, 2016

Documents

Design and Performance Analysis of A Novel Distributed ...web-ext.u-aizu.ac.jp/labs/ce-cc/data/2017.11.02.Submitted-Version... · FIGURE 2: Standard LTE network access procedure with