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International Journal of Computer Applications (0975 8887) Volume * No.*, __(...) Long TermEvolution (LTE) Scheduling Algorithms in Wireless Sensor Networks (WSN)

A.I.A. Jabbar, PH.D Engineering College Department of electrical engineering Mosul University@yahoo.com Fawaz Y. 1 Abdullah Engineering College Department of electrical engineering MosulUniversity Fawaz_yaseen@yahoo.com

Writing issues in this paragraph:1 Incorrect use of sentence fragment

ABSTRACT Wireless sensor network (WSN) is one of the main new areas of future mobilenetworks . There are many applications of WSN such as smart metering, environment monitoring

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and health care . These applications use a large number of sensor nodes , which can generatea huge amount of traffic along with different QoS requirements . Due to the mobility , most of thedata has to be transmitted over wireless networks . Broadband transmission has to be performedthrough the mobile communication systems , which are widely available and deployed . LongTerm Evolution (LTE) are the main promising broadband (...) When incorporating Wireless sensornetworks into the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) (...)networks , one of the challenges is the tra#c 1 overload due to a large number of 2 ( the , a )sensor node . Traffic scheduling plays an important role in LTE technology by assigning the sharedresources among users in the most efficient manner . This paper discusses the performance ofthree types of scheduling algorithms on the downlink were measured in terms of throughput andblock error rate (BLER) using a 3 ( an ) MATLAB-based system level Simulator from the ViennaUniversity. Study an effective radio scheduler to opti(...) General Terms Computer Network,Wireless Network, Modeling, Algorithms Keywords LTE, WSN, scheduling (...) INTRODUCTION

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Wireless sensors may be attached to the network 1 ( system ) through a gateway function ,directly to an eNodB (LTE base station ) or via a range of novel techniques including unstructurednetworking, ad-hoc networking and cognitive radio techniques [1]. In this paper , consideredsensor node (SN) directly connect to LTE. A large number of SNs will be placed in locations wherewireless networks , such as LTE, are the only available method of connectivity. Examples of suchlocations include remote areas (e.g. for Smart grid and agricultural applications ), and inhospitableenvironments (e.g. for industrial applications ). LTE is considered the fourth generation (4G) inmobile communications [1, 2]. It is referred to as mobile multimedia, anywhere anytime, withglobal mobility support , integrated wireless solution , and customized personal service [1]. LTEwill be internet protocol (IP) based, providing higher throughput, broader bandwidth, and betterhandoff while ensuring seamless services across covered areas with multimedia support . Based onthese requirements , the key features of LTE are the usage of multiple access schemes , adaptivemodulation and coding (AMC), multi antenna techniques (MIMO), hybrid automatic repeat request(HARQ) technology and distributed or localized radio resource allocation techniques . The multipleaccess schemes that uses 2 ( use ) LTE are OFDMA with Cyclic Prefix (CP) in DL and SingleCarrier FDMA (SC-FDMA) with CP in UL. OFDM/OFDMA have been selected by 3GPP becauseof its robustness to multipath propagation in wideband channels , inherent support for frequencydiversity and easiness integration with MIMO antenna schemes . Inside each subcarrier AdaptiveModulation and Coding (AMC) is applied with three modulation schemes (QPSK, 16QAM and64QAM) and variable code rates . LTE performs link adaptation via AMC and HARQ in a fast pace(1 ms interval (as known as Transmit Time Interval, TTI)) providing data quickly and reliably usingminimal resources . scheduling 3 ( Scheduling ) is basically 4 ( ___ ) 5 ( primarily ) the processof making decisions by a scheduler regarding the assignment 6 ( job ) of various 7 ( different )resources ( time and frequency ) in a telecommunications system between users. In LTE 8 ( \ )the scheduling is carried out at eNodeB by dynamic packet scheduler (PS) which decides uponallotment of resources to various 9 ( different ) users under its coverage as well as transmissionparameters including modulation and coding scheme (MCS). LTE defines 1 10 ( one ) ms subframesas the Transmission Time Interval (TTI) resulting in the scheduling of resources every one ms. it 11

( It ) means after every 1 ms the assignment of resources could change depending upon various12 ( several ) factors including CQI (Channel Quality Indicator) sent as a 13 ( ___ ) feedback by theuser (SN) to the eNodeB. The process of selecting the transmission parameters and Modulationand Coding Scheme (MCS) is known as Link Adaption (LA). Link adaption 14 ( adoption ) alongwith scheduling of resources is meant to maximize the cell capacity , while making sure that theminimum Quality of Service (QoS) requirements are met and there are adequate resources for best-

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effort bearers with no strict QoS requirements [8]. LA adjusts the data rate with the help of Adaptivemodulation and coding (AMC) by matching the modulation and the channel coding scheme onresources assigned by the scheduler. In situations with advantageous channel conditions , AMCselects a higher modulation order and coding rate and vice versa.

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2 Incorrect verb form with plural subject

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LTE is a promising option for WSN ( environment monitoring applications , 1 ( applications\ ) 2 ( )for example 3 ( \ ) ) because of its higher data rates , lower latency and larger coverage. However ,LTE is not a dedicated technology invented for environment monitoring. Environment monitoringapplications have outstanding Quality of Service (QoS) requirements . Environment monitoringapplications need more stringent latency requirements in network 4 ( system ) than other publicapplications such as web . An excessive delay of the critical data may delay the power restoration ,which may lead to severe economic and social consequences . Reducing latency or end-to-enddelay becomes one of the major challenges in environment monitoring communication networks .In this paper , we 5 investigate the LTE scheduling problem in an environment monitoring WSN. Our 6 contribution is that we study the allocation of resource blocks (RBs) in the eNodeB 7 ( \ )in LTE, to study scheduling time guarantee to different classes of environment monitoring traffic .This paper is organized as follows . In Section 2, presents an overview of LTE packet schedulingmodel . We 8 describe the related works in Section 3. The selected simulation scenarios and thecorresponding results are presented and analyzed in Section 4 and Section 5 concludes the paper2. LTE DOWNLINK RADIO RESOURCE SCHEDULING Scheduling in the downlink LTE system isperformed at 1 TTI, which consists of 2 9 ( two ) time slots , or resource-block-pair basis (RB, onesubframe of 0.5ms over 180 kHz). Within this TTI, two consecutive RBs are assigned to a user [28].In each TTI, each SN computes its received signal(...)

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4 Overused word: network

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Figure 1:Packet Scheduling operation [19]

The overall goal of most scheduler is satisfy 1 ( is satisfied ) the system and users requirement .A good scheduling algorithm therefore has two main objectives(...) Round Robin (RR) is one ofthe fundamental and widely used scheduling algorithms. Its running process is very simple andeasy to implement . The RR algorithm [30] assigns equal portions of packet transmi(...) achievesthe best fairness performance if the users have similar channel conditions and similar sized packetarriving at their buffers . It is on a first come first served basis . Although RR gives every UE anequal chance 2 ( opportunity ) to obtain RBs, the overall throughput is much lower than in other schedulers 3 ( schedules ) because this scheduler does not take the channel conditions into itsconsiderations . In LTE, different UEs have different services with different QoS requirements andit is very difficult to allow every UE to take up the same RBs for the same possibility because itwill decrease the resources 4 ( resource's ) 5 ( resource ) efficiency . 2.2 Best CQI Scheduler(BCQI): Best CQI scheduling scheme 6 ( plan ) optimizes the UE throughput by assigning theRBs to the UE with the best radio link conditions , meaning that the UEs with low CQI value have 7 ( a , the ) lower chance to be served [8]. These CQI informations 8 ( information, formations, informations, informatics, informative ) contain the value of the signal-to-noise and interference ratio(SINR) measured by the UE. A higher value of CQI indicates a better channel condition . The bestCQI is selected for scheduling based on the CQI received . BCQI scheduling scheme can increasecell throughput at the expense of worst fairness . In this scheduling mechanism , UEs located farfrom the base station are unlikely to be scheduled . 2.3 Proportional Fair Scheduler (PF): Thisalgorithm assigns the PRBs to the UE with the best relative channel quality i.e. a combination ofCQI & level of fairness desired . There are various versions of PF algorithm based on valuesit takes into account . Main goal of this algorithm is to achieve a balance between Maximizing thecell throughput and fairness , by letting all users to achieve 9 ( achieve ) a minimum QoS (Qualityof Service). 2.4 Max-Min scheduler Maximizing the minimum of the UE throughputs is the maintask of MaxMin scheduler. Max-Min scheduler allocates the resources in a way that 10 ( an , the )equal throughput for all users is guaranteed . The scheduler maximizes the minimum of the UEthroughputs [4]. Max-Min scheduler is Pareto optimal, meaning that the rate of one UE cannot beincreased without decreasing the rate of another UE that has a lower rate than the one considered .2.5 Resource Fair Scheduler (RF): The RF scheduler scheme 11 ( plan ) allocates an equal amountof resources for all UEs. It mainly aims to maximize the sum rate of all UEs while ensuring fairnesswith respect to the number of RBs assigned to a UE. 2.6 Max. Throughput Scheduler(KMT) TheKMT scheduler performs assignment of resources in two steps in order to reduce complexity. In thefirst step , each two RBS (i.e SB) are assigned to the UE who can 12 ( Can ) support the highestbit rate . In the second step , the best MCS for each UE is determined . The idea behind KMTscheduler is to assign a disjoint subset of SBs to each UE, thereby a joint multiuser optimizationproblem is reduced into U ( number of simultaneous users) parallel single-user optimizationproblems . [14] gives 13 ( provides ) more details about this scheduler 14 ( schedule ) .

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3. Related Work Since the LTE technical standard does not define a unique scheduling algorithm,the LTE scheduling has attracted significant attention from researchers. In [8], YuzheXu et al.investigated the latency performance of LTE network in smart grid , and proposed a new LTEscheduler that firstly allocates resources for smart grid . In [9], Yasir et al. presented a Bandwidthand QoS Aware (BQA) LTE uplink scheduler, which maximizes the cell throughput by giving priorityto user equipments (UEs) with better channel conditions and maintains some level of fairness byproviding resources to UEs with adverse channel conditions . In [10], The LTE downlink has beenpreviously analyzed in several papers like [3], [4] and [5]. The authors evaluated the systemthroughput and the fairness of the scheduling algorithms used in their simulations, but thework was restricted either to SISO (Single Input Single Output) antenna technology, staticusers, one system bandwidth and none of them were based on a simulator with the radioenvironment modeled . 1 Moreover , the user throughput and BLER (Block Error Rate) and theCQI values for a particular UE (User Equipment) over the simulation period have not been treated .4. SIMULATION OVERVIEW In order to highlight the effect of different schedulers on the individualSN as well as the whole LTE network 2 ( system ) throughput and BLER performance , the LTEsystem level simulator introduced in [7] was used with simulation parameters shown in Table 1.Table 1. Simulation Parameters Parameter Value Frequency 2.1 GHz LTE bandwidth 10MHzNumber of RBs, N 100 Number of subcarriers 600 Number of SNs varies Number of BS 1 ChannelModel 3GPP TU Simulation length 1000 TTI SNs position SNs located in target sector 3 ( area )only , Antenna setup 1 transmit, 1 receive (1 x 1) Receiver Zero Forcing ZF Schedulers RoundRobin (RR) Best CQI (BCQI) Proportional Fair (PF) Max. Throughput (KMT) Resourc Fair (PF) (...)

Writing issues in this paragraph:1 Wordy sentence

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4.2 Performance Measures Two aspects of the performance are investigated in the thesis : bit rateand accuracy . The rate of bit is shown by the throughput value and the accuracy is measuredby the BLER. Now we 1 give a brief introduction to the concepts of throughput and BLER. 4.2.1Throughput This 2 is measured as the total number of bits successfully transmitted over the airinterface from the UE up to the eNodeB over the total simulation time . That means the systemaverage throughput is the sum of average throughput of # all users. Throughput= B/tsim WhereB represents the total amount of received bits and tsim 3 ( term, trim, tem, tam, tum ) representsthe total simulation time . 4.2.2 BLER Except the high throughput, the accuracy of the receiveddata is very important 4 ( crucial, critical, imperative, paramount ) 5 ( \ ) as well . BLER is one wayto show this accuracy , and it is the ratio between the number of erroneous blocks and the totalnumber of received blocks . It is referred to as packet error rate . The lower the BLER value is ,the better the system performance will be, but the performance is acceptable if the BLER value isunder a specific 6 ( particular ) threshold3 [1, pp.283]. If the BLER is too high , there will be toomany errors in the transmitted data we 7 received , and the data will be useless . 5. RESULTS ANDANALYSIS In order to highlight the relationship between Throughputs and Block error rate (BLER)with the respective CQI levels, the first experiment was run as follows . Using PF scheduler , 8

( scheduler\ ) there are 15 CQI levels in the LTE network and the simulator generates #30 SNs andthey are distributed in the cell as shown on Fig. #2. By selecting any SN whose located is very closeto eNodeB such as SN#9, as it is shown in Fig #4, a high throughput was achieved corresponding

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to a CQI level of 15 ( see Fig.#3 for CQI distribution of node SN#9). In other words , level 15 of CQIis used most of the time and is frequently updated to the eNodeB 2. This 9 means that the SN9 is ina very good condition to receive the maximum date rate . Such high CQI level is also reflected in thelow BLER due to channel conditions as will be shown later .

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Fig. 2. The distance between eNodeB and UE. 1 Red dot represents eNodeB (eNodeB2 sector2 ( area ) 1 is active) and blue dot represents SN. Fig. 3. CQIs distribution of SN19 close to eNodeB2, RB25.

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Fig. 4. Throughput, BLER when SN19 near eNodeB 2.