Receved: July 28, 2018 209 New Method Based on Prorty of Heterogeneous Traffc for Schedulng Technques n M2M Communcatons over LTE Networks Maryam Ouassa 1 * Abdallah Rhattoy 2 1 Informaton and Communcaton Systems Engneerng Research Group, Hgh School of Technology, Mathematcal Modelng and Computer Scence Laboratory, Ecole Natonale Supéreure des Arts et Méters, Moulay-Ismal Unversty, Meknes, Morocco 2 Department of Computer, Informaton and Communcaton Systems Engneerng Research Group, Hgh School of Technology, Moulay-Ismal Unversty, Meknes, Morocco * Correspondng author s Emal: maryam.ouassa@edu.um.ac.ma Abstract: Machne to Machne (M2M) communcaton s a technology for machnes communcatng wth each other wthout human nterventon. Long Term Evoluton (LTE) and Long-Term Evoluton-Advanced (LTE-A) cellular networks technologes provde the resources requred for M2M communcaton. However, LTE s requred to meet M2M communcaton requrements, such as power management and support for a large number of devces whle mantanng the Qualty of Servce (QoS) requrements for these devces. Resource allocaton or schedulng s one of the key challenges facng M2M communcatons over Long-Term Evoluton networks. M2M traffc has unque characterstcs; t usually conssts of a large number of small data packets, wth specfc delays, generated by a potentally large number of devces competng on scarce rado resources. In ths artcle, we wll present the schedulng technques on the Packet Downlnk Control Channel (PDCCH) n a M2M scenaro based on the prorty and dfferentaton between heterogeneous traffc (real tme and non-real tme) to acheve hgh spectral effcency n cellular systems and provde qualty of servce guarantees for system users. The results obtaned show that the FLS downlnk scheduler s the optmal soluton based on our proposed method whch offers more prorty to the real tme servce (vdeo and vop) than the non real tme servce (best effort). Keywords: M2M, MTC, H2H, LTE, LTE-A, Schedulng, QoS, Resource allocaton. 1. Introducton Machne to Machne (M2M) communcaton [1] s a new paradgm that allows a myrad of machnes to communcate wth each other autonomously. M2M thus allows the emergence of new servces belongng to dfferent felds of applcaton such as transport, health and survellance thus mprovng the daly lfe of human. M2M has been standardzed n Long Term Evoluton (LTE) cellular networks by Machne Type Communcatons (MTC) [2, 3]. The desgn of the LTE cellular system s used for Human to Human (H2H) communcaton. Therefore, t s consdered that the M2M applcaton platform s napproprate. Its obvous features nclude for example, the number of devces n M2M communcatons s greater than that of H2H communcatons. It also contans a small amount of data and a large number of nodes, dfferent delays and dversfed servces whch can cause a sharp ncrease or decrease n traffc [4]. Therefore, as a M2M communcaton network, many problems are encountered by the moble communcaton network desgned based on the characterstcs of H2H. In order to realze that t can effcently provde a M2M communcatons servce, the exstng moble communcaton network must be optmzed based on the M2M communcaton applcatons to avod congeston or network overhead caused by smultaneous access of huge amounts of M2M users to the network [5, 6]. As a result, many standards organzatons and
Receved: July 28, 2018 210 nternatonal projects are makng great efforts to mprove the support of LTE cellular networks for M2M applcatons. A key feature of the LTE network s the adopton of a number of advanced rado resource management procedures to mprove system performance and support user needs for Qualty of Servce (QoS), especally for real tme flows to know voce and vdeo. In partcular, packet schedulng mechansms play a fundamental role because they are responsble for choosng, carefully and n a precse tme, how to dstrbute the rado resources between the dfferent statons takng nto account the channel condtons and requrements of QoS [7]. In ths context, we are nterested n the ssues of rado resource allocaton and ts mpact on QoS wthn a LTE canddate technology for M2M communcaton. The purpose of our work s to create a M2M scenaro over LTE downlnk network by addng a servce prorty method between realtme and non-real tme traffcs n order to smulate the man downlnk schedulng technques of whch the goal s to fnd the best scheduler that offer an acceptable QoS level accordng to a prorty and a dfferentaton between servces, where real-tme servce s certanly pror to the non-real-tme servce and the allocaton of resources for the servce n real tme s prortzed at non real tme. Moreover, the proposed scheduler amed n partcular at mprovng the performance metrcs for real tme servces and mantanng n general a satsfactory level of the performance metrcs for the other servces n the network smultaneously. The rest of the paper s organzed as follows: n secton 2, we presented the M2M system archtecture, as well as access technques for LTE technology focusng on the schedulng technque. We wll also explan the resource allocaton for M2M communcatons. The thrd secton deals wth the procedure and the polces of the schedulng for the downlnk drecton as well as the study of the dfferent algorthms used by the LTE technology. In the fourth secton, we dscussed our method based on servce prorty. We Fgure.1 M2M system archtecture descrbe n secton 5 the evaluaton of schedulng algorthms performance wth the method proposed, we analyze the mpact of each algorthm on the QoS for each type of real-tme traffc (vop and vdeo) and non real-tme (best effort) and we compare the results of the smulatons by ndcatng the best algorthm n terms of the followng metrcs: Throughput, Goodput, Farness, Delay, Packet Loss Rate (PLR) and Spectral Effcency. 2. System model 2.1 M2M system archtecture Fg. 1 shows the archtecture of an M2M system, whch has three man parts: the MTC devces, the communcaton network and the MTC server [8]. MTC devces are termnals that provde nformaton, often n real tme, devces nvolved n M2M or MTC communcatons are generally referred to as Machne Type Communcatons Devces (MTCD). Unlke H2H communcatons, M2M communcatons are typcally characterzed by massve access combned wth small payloads of data. MTCD data can be generated by event trggerng or as perodc reports. For the communcaton network, there are several technologes that can be consdered as needed. LTE s the latest evoluton of moble phone standards defned by the 3rd Generaton Partnershp Project (3GPP). The network conssts of two parts: the rado access network Evolved UMTS Terrestral Rado Access Network (EUTRAN) and the core network Evolved Packet Core (EPC). The core network EPC uses all-ip technology [9]. The M2M server manages vsualzaton, data analyss and the resultng decson-makng. Wth the rapd development of the Long Term Evoluton of cellular networks and ts evoluton Long Term Evoluton-Advanced (LTE-A), M2M communcatons over extended coverage LTE- Advanced cellular networks should be an mportant
Receved: July 28, 2018 211 part of Internet of Thngs (IoT). LTE networks are desgned to support packet swtchng wth seamless moblty, QoS provsonng and mnmal latency. LTE transmssons are organzed nto rado frames of 10 ms, each frame dvded nto ten subframes of 1 ms. The subframes are dvded nto two slots of 0.5 ms. The transmssons are multplexed usng Sngle Carrer Frequency Dvson Multple Access (SC- FDMA) n the Uplnk (UL) channel, whle Orthogonal Frequency Dvson Multple Access (OFDMA) s used n the Downlnk (DL) channel. Schedulng s the process mplemented by Evolved NodeB (enodeb) to allocate rado resources based on human User Equpment (UE) or MTCD requests n the downlnk or uplnk drecton. The mnmum rado resource unt that can be allocated to a devce s called the Physcal Resource Block (PRB), whch s composed of two slots n the tme doman and 12 subscrbers that correspond to 180 khz n the frequency doman [10, 11]. Fgure.2 Poston of scheduler n LTE 2.2 Downlnk Schedulng Process Scheduler s the functonal entty for obtanng fast packet schedulng and the basc MAC layer functon n LTE enodeb. Ths has a major mpact on system performance. It contans an uplnk scheduler and a downlnk scheduler, whch support the allocaton of physcal layer resources for Uplnk Shared Channel (UL-SCH) and Downlnk Shared Channel (DL-SCH) respectvely. Physcal layer resources nclude prmarly PRB, Modulaton Codng Scheme (MCS), power allocaton schemes, and antenna selecton schemes for multple antennas. Fg. 2 shows the poston of the scheduler n the LTE protocol and ts relaton to key technques such as Hybrd Automatc Repeat Request (HARQ) and Adaptve Modulaton and Codng (AMC). Its key functon s to determne how to properly allocate the resource to users n order to maxmze system throughput, as long as the tme and packet QoS are guaranteed [12, 13]. The qualty of the channel s necessary n the schedulng. The enodeb gets t by recevng the notfcaton of the user's channel qualty ndcator. As shown n Fg. 3, the downlnk scheduler allocates resources to users based on ther channel qualty, QoS needs and farness. enodeb sends date and schedulng sgnalng based on schedulng results. Scheduled users accept data based on BR assgnment results, MCS optons and antenna selecton n downlnk schedulng sgnallng [14]. Fgure.3 Downlnk Schedulng Process 2.3 Resource Allocaton for M2M Communcatons The classfcatons of LTE / LTE-A schedulng technques are based on M2M communcaton requrements to acheve optmal ntegraton. Based on these characterstcs, we can conclude that M2M communcatons requre four man requrements. Frst, energy-effcent schedulng uses SC-FDMA for the uplnk and OFDMA for the downlnk. Second, QoS-based plannng s requred to handle dfferent QoS requrements such as latency, jtter, error rate, and Guaranteed Bt Rate (GBR). Thrd, mult-hop based schedulng uses several hops to send data over short dstances nstead of sendng them over long dstances usng a sngle hop, whch saves energy. Fnally, scalable network based schedulng uses the natve IP LTE-A connectvty
Receved: July 28, 2018 212 feature to support a consderable number of M2M communcatons [15]. Schedulng s the process mplemented by enodeb to allocate rado resource PRB to UE or MTCD n the downlnk or uplnk drecton. The PRB s a resource grd consstng of 12 subcarrers wthn a tme nterval. The schedulng process can be dvded nto two stages: Tme Packet Packng Schedulng (TDPS): In ths step, the enodeb selects a termnal (UE or MTCD) or a group of termnals to whch PRBs must be allocated accordng to certan crtera, for example: channel status, QoS and equty. Frequency Doman Packet Schedulng (FDPS): In ths step, the enodeb selects the PRBs to assgn to the termnal or group of termnals that were selected n the TDPS step. The enb allocates PRBs that the termnal can use to the maxmum. For example, t can allocate the PRBs for whch the gven termnal has the best channel condtons [16]. The desgn of downlnk schedulng technques for M2M communcatons should take nto account the fact that MTCDs n M2M communcatons may have strct delay requrements. In addton, schedulng should take nto account the fact that a sgnfcant number of MTCDs can contend for lmted rado resources [17]. 3. Downlnk resource schedulng algorthms Packet Schedulng mechansms play a fundamental role n the rado resource management (RRM) n cellular networks as they am to optmze, accordng to specfc crtera, access to tme and frequency resources. These mechansms allow the allocaton of rado resources by takng nto consderaton certan parameters relatng to the state of the transmsson channel and to the users' requrements n terms of QoS, especally for realtme streams, namely the voce and the vdeo streams. The purpose of rado resource allocaton algorthms s to mprove system performance by ncreasng spectral effcency and network equty [18]. It s therefore essental to fnd a compromse between effcency (ncrease n throughput) and equty between users. The man purpose of ths type of algorthm s to maxmze the overall system throughput. Several algorthms use ths approach as: Proportonal Far (PF), Maxmum Largest Weght Delay Frst (MLWDF), Exponental Proportonal Far (EXP/PF), Frame Level Scheduler (FLS), Exponental (EXP) RULE and Logarthmc (LOG) RULE [19]. 3.1 Proportonal far It s an opportunstc schedulng algorthm. Ths type of algorthm uses nfnte queues; these queues are used n the case of non-real tme traffc. Its purpose s to try to maxmze the overall throughput of the system by ncreasng the throughput of each user at the same tme; t tres to ensure equty between users, the objectve functon representng the PF algorthm s: a = d (t) d (1) Where d (t) s the rate correspondng to the CQI of the user and d s the maxmum rate supported by the Resource Blocks (RB). 3.2 Exponental proportonal far Ths s an mprovement of the PF algorthm that supports real-tme flows (multmeda); by the way, t prortzes real tme flows over others. A user k s desgnated for the schedulng accordng to the followng relaton: k = max a d (t) exp ( a W (t) X d 1 + X ) (2) N X = 1 N a W (t) =1 (3) Where W (t) s the tme tolerated by the flow and a s strctly postve settng for all. 3.3 Maxmum largest weghted delay frst M-LWDF s one of the algorthms consderng tme lmts. Ths type of algorthm deals wth delays n arrvng and delverng packets, desgned prmarly to handle real-tme flows (multmeda and vop). If a packet exceeds these tolerated delay values, t wll be removed from the lst of flows to schedule whch sgnfcantly degrades the QoS. Ths algorthm supports flows wth dfferent QoS requrements; t tres to weght packet delays usng channel state knowledge, at a tme t, the algorthm chooses a user k for schedulng va the formula: k = max a d (t) W d (t) (4) Ths s practcally the same formula of the EXP-PF algorthm, except that:
Receved: July 28, 2018 213 a = log(p ) T (5) Where p s the probablty that the delay s not respected and T s the tme that the user can tolerate. Ths algorthm s manly amed at the real-tme flow whch requres the respect of the deadlnes, t gves good results n ths context, by cons for nonreal tme flows, and t s really not a good choce because the delay s really not an mportant parameter. 3.4 Frame level scheduler FLS s a mult-class schedulng algorthm; ths scheduler class consders classes of queued flows n order to execute the approprate schedulng polcy for each class. The type of flow (whether real tme or non-real tme) s a fundamental parameter for ths type of algorthm. Before makng the resource allocaton decson, the servce type must be nspected before allocatng the approprate resource blocks for the broadcast. On the other hand, n spte of the prortzaton of the real-tme flows, the classcal flows wll not be neglected and removed from the queue n the event of congeston. FLS s a scheduler that consders qualty of servce and s manly used for real-tme communcatons n LTE networks. Its schedulng scheme s dvded nto two levels, whch nteract wth each other to allow the dynamc allocaton of resource blocks to users. In the upper level, a less complex algorthm based on a lnear control loop and dscrete over tme; ths level specfes the number of bts contaned n the packet to be transmtted by the source n the LTE frame. In the lower level, the rado resources are allocated to the users usng the schedulng strategy PF takng nto account the bandwdth requred by the FLS scheduler. Ths two-level modelng provdes a compromse between system throughput and equty. For the scheduler PF the volume of the data u (k) for the flux n the k LTE frame s calculated accordng to the followng formula: u (k) = h (k) q (k) (6) In the precedng formula, the volume of data to be transmtted s the convoluton n the dscrete tme (*) between the level of the queue q (k) and the mpulse response of the lnear flter used denoted h (k). 3.5 Exponental RULE The exponental scheduler s consdered an enhancement of the EXP/PF scheduler; t uses at most the parameters used n EXP/PF: the spectral effcency of the user s one of the optmzaton parameters that are set by report to the need of the network. The EXP-RULE provdes qualty of servce guarantees over a wreless lnk. EXP-RULE selects a sngle user per queue to receve the servce n each schedulng nstant. EXP-RULE s based on the followng expresson: w,k EXPrule α D HOL, = b exp c + ( 1 ) D ( N HOL, rt ). Γ k (7) Where N rt represents the number of actve real tme flows, D HOL, represents the tmeout of the packet n the queue and Γ k s the spectral effcency of the user for the flow k. For optmal results, the parameters α, b and c are defned as follows: 5 α [ (0.99τ ), 10 (0.99τ ) ] b = 1/E[Γ ] { c = 1 (8) It should be noted that the EXP-RULE scheduler takes nto account the delay taken by a user's packet and normalze t wth respect to the overall delay experenced by the UEs of the cell. The advantage of ths scheduler s that t reles on the state of the network (here the LTE cell) before approachng the schedulng process. 3.6 Logarthmc RULE LOG-RULE s a scheduler that ensures a balance n the qualty of servce parameters n terms of average delay. Ths algorthm s based on the same parameters used n the EXP-RULE scheduler; however, the scheduler metrc s computed from the logarthm of the user's flow. The LOG-RULE s represented as follows: w,k LOGrule = b log(c + α D HOL, ). Γ k (9) Where D HOL, represents the tmeout of the packet n the queue, and Γ k s the spectral effcency of the user for the flow k.
Receved: July 28, 2018 214 The farness and optmal bt rate of the LTE cell are obtaned by choosng the rght optmzaton parameters α, b and c. For optmal results, the parameters α, b and c are defned as follows : 5 α (0.99τ ) b = 1/E[Γ ] { c = 1.1 4. Proposed method (10) In ths secton, we propose a resource schedulng method for the downlnk n LTE for M2M communcatons. Ths method provdes a QoS guarantee for real-tme servces and s also sutable for non-real-tme servces. Dfferent servces have QoS requrements n the LTE system; on ths bass 3GPP dvdes them nto several types. Gven dvson, we prortze dfferent types of servces to ensure real-tme servce delvery wth hgh QoS requrements. At the same tme, we take nto account the prorty of the logcal channel of the Meda Access Control (MAC) layer and consder the user's channel qualty to obtan a hgher cell rate. Exstng schedulng algorthms ncludng PF, MLWDF, EXP PF, FLS, EXP-RULE and LOG- RULE have a common dsadvantage. None of them takes nto account the QoS needs of the user's servces. Sometmes there are a lot of users n the system and non-real-tme servces need too many resources, n whch case ths type of algorthm cannot guarantee real-tme servce performance. There are certan algorthms consderng the qualty of servce of the user for the OFDMA system. One s PF, the user wth a maxmum packet transmsson delay at the hghest prorty, and ths algorthm gves hgh results n terms of delay and packet loss rates. Consderng that t can nether guarantee the flow of the system, nor be sutable for real-tme servces. Therefore, accordng to the lmtaton of the method above, we propose a method of schedulng algorthm. It combnes QoS prorty wth MAC servce and logcal channel prortzaton wth user channel qualty ndcaton and user equty. It apples not only to real-tme servces but also to non-realtme servces. The resource allocaton method that we wll descrbe performs the schedulng functon of Fg. 3. In the proposed method, packet schedulng executes resource allocaton decsons every 1 ms whch s defned as the Transmsson Tme Interval (TTI), for real-tme servce s certanly pror to the non-realtme servce and the allocaton of resources for the servce n real tme s prortzed at non real tme. We consder n ths method of resource allocaton, RBvdeo s the number of resources allocated for vdeo traffc wth RBvdeo_max s maxmum number of resources that can be assgned to users who uses vdeo traffc, RBvop s the number of resources allocated for vop traffc wth RBvop_max s the maxmum number of resources that can use vop traffc and RBnon_real_tme these are the resources allocated for non real-tme traffc wth RBnon_real_tme_max s the maxmum number of resources that can use non-real-tme traffc. The dagram of the proposed process s llustrated n Fg. 4. We wll present our resource schedulng algorthm n two steps. Resource allocaton for real-tme servces: If there are real-tme servces, we allocate resources to them preferentally. If all avalable real-tme resources are exhausted, then serve the non-realtme servces n turn. When allocatng resources for real-tme servces, we must take nto account the qualty of user channels, the delay of packet transmsson and the prortzaton of logcal channels. Resources allocaton for non-real-tme servces: We are startng to allocate resources for non-realtme servces after all real-tme servces have been served or the real-tme avalable resource s exhausted. Gven ther low demand for QoS and to reduce complexty, we refer to delay and loss rate factors. If all non real-tme servces have been served before the non-real-tme resource s exhausted, the rest s real-tme. 5. Results and dscusson In ths secton, we evaluate the performance of our proposed method by applyng t on the exstng LTE downlnk schedulers whch are PF, MLWDF, EXP/PF, FLS, EXP-RULE and LOG-RULE, already studed n secton 3. Accordng to ths exstng works [20-22] concentrated on LTE schedulng downlnk algorthms, the authors compared and analyzed LTE downlnk schedulers appled at the level of specfc scenaros and separate traffcs ether n real tme or n non real tme, therefore, ths s not applcable n an envronment wth multple servces for heterogeneous users. So, the objectve of our work s to propose a new method for M2M communcatons, whch meets ths requrement by applyng t on LTE downlnk schedulers n order to conclude whch s the most effcent and performng algorthm whch acheve hgh spectral effcency n cellular systems, maxmzng throughput and provde qualty of
Receved: July 28, 2018 215 servce guarantees for system users, by makng a dfferentaton between the traffcs and especally we gve more prorty to real tme traffc than non real tme traffc. Vdeo and vop were selected as real-tme traffc and Best Effort (BE) for non-real tme. We mplemented our new scheme n a M2M scenaro based on the prorty and dfferentaton between heterogeneous traffc usng an open source LTE-Sm smulator. LTE-Sm s a LTE network event smulator, developed n C++ language. In addton, t supports multple scenaros, sngle and mult-cell envronments, QoS management, user moblty, transfer and t was desgned to perform smulatons for dfferent uplnk and downlnk schedulng strateges [23]. Fgure.4 Dagram of the proposed method
Receved: July 28, 2018 216 Table 1. Smulaton Parameters Smulaton Parameters Values Smulaton duraton Flow duraton Frame structure Moblty Cell radus System Bandwdth RB Bandwdth Tme Slot Schedulng Tme (TTI) Number of RBs 120s 120s FDD Random drecton 1Km 20 MHz 180 khz 0.5ms 1ms 100 RBs Maxmum delay 0.1s Vdeo Bt Rates VoIP Bt Rates 242 kbps 8.4 kbps Number of MTCDs 0 à 100 Fgure.5 Average delay aganst number of MTCDs In our smulaton, we consdered the case of a sngle cell wth nterference; we used an envronment of a cell wth a radus of 1 klometer n whch a set of MTC users set chosen n the range [0-100] and unformly n random drecton moblty. The purpose of ths smulaton s to evaluate the performance of the LTE network n hgh congeston M2M scenaro based on the method of prorty and dfferentaton between real tme and non-real tme traffcs proposed n ths paper, users are unformly dstrbuted and for every two users transmttng vdeo traffc and two users transmttng vop traffc there s one user transmttng BE traffc. Our evaluaton s based on schedulers mplemented n the base statons of the LTE network whch are PF, MLWDF, EXP/PF, FLS, EXP-RULE and LOG- RULE by the measurement of Throughput, Goodput, Farness, PLR, Delay and Spectral Effcency [24, 25]. The smulaton parameters are llustrated n the followng table. The delay has a very mportant nfluence on the performance of the network. Latency s the transt tme of a packet from enodeb to the devce. It depends on the nature of the applcatons present n a network that the degree of requrement changes lkes propagaton tme, processng tme and packet sze. As can be seen n Fg. 5, for the schedulng algorthm PF, the delay s n hgh ncrements untl becomng ntolerable wth a sgnfcant tmeout value because the PF algorthm satsfes the servce schedulng requrements not real tme, but s not deal for real-tme servces. Ths s because the PF Fgure.6 Packet Loss Rate aganst number of MTCDs scheme dd not take nto account the delay of the data packet whch s one of the attrbutes of the LTE network. For the other algorthms studed, the delay remans the weakest and s neglgble and nsenstve to the ncrease of devces n the cell. The PLR Estmaton mprovement s a crtcal ssue because ts value has a bg effect on network performance, especally when t comes to real-tme traffc lke voce over IP and vdeo. Packet loss s the lost bytes when transmttng packets. It expresses tself n rate of loss. Rather, t corresponds to the number of data packets that were not receved by the destnaton durng a communcaton. Ths can be caused by many factors, mostly due to network congeston, as t can be caused by packet latency. Fg. 6 represents the evoluton of the packet lost rate as a functon of the number of MTCDs n the coverage area of the cell, when the number of actve devces ncreases n the cell, the loss rate ncreases,
Receved: July 28, 2018 217 Fgure.7 Average throughput aganst number of MTCDs Fgure.8 Average goodput aganst number of MTCDs n partcular for the PF algorthm whch could reach a maxmum rate of 60%. Fg. 7 llustrates the average throughput as a functon of numbers of MTC devces, the measurement of ths metrc s one of the mportant operatons whch makes t possble to dentfy the average success rate of transfer of the messages on a communcaton channel, ts value s calculated for a tme nterval, by dvdng the total amount of nformaton receved durng ths nterval by the duraton of the nterval n queston. As shown n Fg. 7, the FLS algorthm offers the largest throughput for the vdeo stream case, followed by the EXP- RULE. The EXP-PF, MLWDF and LOG-RULE algorthms are very comparable n terms of throughput performance that starts to decrease from 80 devces. For the PF algorthm, ts bt rate also decreases n the same drecton as the other algorthms but from 40 devces and ts value s much lower ths s explaned by, when the number of MTC devces s ncreased, the delay (Fg. 5) and packet loss rate (Fg. 6) ncrease. The decrease n the throughput s due to the unavalablty of block resources suffcent to serve all users. Goodput s the average rate of a successful transmsson of data over a communcaton channel. In our smulaton, ths measure measures only the total data rate on the network, gnorng all other headers. A user's goodput s measured by frst countng the total number of successfully receved data packets and calculatng the number of bts receved, whch s ultmately dvded by the total executon tme of the smulaton. Fg. 8 shows that the FLS algorthm offers better performance n terms of goodput compared to other algorthms that have almost the same values where the goodput decreases accordng to the number of MTC devces n the cell. We also note that the PF s the least effcent wth regard to the metrc PF. Fg. 9 shows the calculated farness ndex for our scenaro whch s obtaned by consderng the throughput acheved by each flow at the end of each smulaton, t s noted here that the farness rates through the EXP-PF, MLWDF, FLS EXP-RULE and LOG-RULE are almost smlar and beleve n the densty of the cell, reachng almost 90% equty between dfferent users. On the other hand, the PF decreases from 60 MTCDs. Spectral effcency s defned as the maxmum user rate dvded by the bandwdth of the channel; ths s the number of receved bts correctly normalzed by the resource consumed n tme and bandwdth. Thus, spectral effcency s strongly related to resource consumpton and packet error rate. As shown n Fg. 10, the MLWDF algorthm has a better spectral effcency compared to that measured for the other algorthms. Ths effcency decreases for all the algorthms as the number of devces ncreases. Fgure.9 Farness ndex aganst number of MTCDs
Receved: July 28, 2018 218 Fgure.10 Spectral effcency aganst number of MTCDs 6. Concluson In ths paper, a new resource schedulng method based on the prorty and dfferentaton of servces for M2M communcatons n LTE downlnk networks has been proposed. We consder two types of traffc: real-tme traffc (vdeo and vop) and nonreal-tme traffc (best effort), we assgn RBs to MTC devces that use real-tme servce ahead of non-real tme to satsfy servce QoS n real tme. We presented a comparatve analyss of sx rado resource allocaton algorthms based on the proposed method, n order to establsh the mpact of these resource allocaton algorthms on the qualty of servce of dfferent applcatons for a M2M scenaro n LTE networks. Smulatons were made n terms of throughput, goodput, delay, farness, packet loss rate and spectral effcency to conclude the most effcent algorthm. We can notce that the FLS downlnk scheduler based on the proposed prorty method n our smulaton has better performance due to ther characterstcs of less delay and PLR, hgh throughput, goodput and farness. We can conclude that the FLS algorthm s the sutable scheduler n a hybrd envronment wth heterogeneous traffc and where we prortze the real tme traffc that non real tme traffc. For future work the performance of dfferent schedulng algorthms on LTE network for M2M communcatons can be analysed under dfferent applcatons (CBR, VBR, UBR and ABR). References [1] J. Km, J. Lee, J. Km, and J. Yun, M2M servce platforms: Survey, ssues, and enablng technologes, IEEE Communcatons Surveys Tutorals, Vol.16, No.1, pp.61-76, 2014. [2] T. Taleb and A. Kunz, Machne type communcatons n 3GPP networks: Potental, challenges and solutons, Communcatons Magazne, Vol.50, No.3, pp.178-184, 2012. [3] M. Ouassa, M. Benmoussa, A. Rhattoy, M. Lahmer, and I. Chana, Performance Analyss of Random Access Mechansms for Machne Type Communcatons n LTE Networks, In : Proceedngs of the Int. Conf. on Advanced Communcaton Systems and Informaton Securty 2016 (ACOSIS'16), Marrakech, Morocco, 2016. [4] M. Ouassa, M. Benmoussa, A. Rhattoy, M. Lahmer, and I. Chana, Impact of M2M Traffc n Random Access Channel over LTE Networks, In: Proc. of the 1st Sprnger Internatonal Conference on Emergng Trends and Advances n Electrcal Engneerng and Renewable Energy, Inda, 2016. [5] A. Bral, M. Centenaro, A. Zanellan, L. Vangelsta, and M. Zorz, The challenges of M2M massve access n wreless cellular networks, Dgtal Communcatons and Networks, Vol. 1, No. 1, pp.1-19, 2015. [6] 3GPP TS 22.368. V14.0.1, Servce requrements for Machne-Type Communcatons (MTC); Stage 1 (Release 14), 2017-08. [7] A. Ajaz, and A. Hamd Aghvam, On Rado Resource Allocaton n LTE Networks wth Machne-to-Machne Communcatons, In: Proc. of the 2013 IEEE 77th Vehcular Technology Conference (VTC Sprng), Dresden, Germany, 2014. [8] F. Ghavm and H. H. Chen, M2M Communcatons n 3GPP LTE/LTE-A Networks: Archtectures, Servce Requrements, Challenges, and Applcatons, IEEE Communcatons Surveys Tutorals, Vol. 17, No. 2, 2015. [9] G. Frtze, SAE: The Core Network for LTE, 2012. [10] S. Sesa, I. Toufk, and M. Baker, LTE-The UMTS Long Term Evoluton: From Theory to Practce, 2nd edton, 2011. [11] M. Coupechoux and P. Martns, Vers les systèmes rado mobles de 4e génératon - de l'umts au LTE, 2013. [12] 3GPP TS 36.300. V15.2.0, Evolved Unversal Terrestral Rado Access (E-UTRA) and Evolved Unversal Terrestral Rado Access Network (E-UTRAN); Overall descrpton; Stage 2 (Release 15), 2018.
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