Nonorthogonal Direct Access for Small Data Transmission in Cellular MTC Networks


 Lorin Briggs
 3 years ago
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1 Nonorthogona Direct Access for Sma Data Transmission in Ceuar MTC Networks KengTe Liao, ChiaHan Lee, TzuMing Lin, ChienMin Lee, and WenTsuen Chen Academia Sinica, Taipei, Taiwan Industria Technoogy Research Institute, HisnChu, Taiwan Abstract In conventiona LTE networks, user equipments (UEs) access the network via a fourstep random access procedure. When sma amount of data is transmitted, such as in the scenarios of machinetype communications (MTC), signaing overhead resuted from the random access procedure becomes a critica issue. To enabe an efficient sma data transmission, a direct access scheme utiizing code division mutipexing (CDM) is proposed in this paper. Instead of transmitting on the resource bocks indicated by enb, UEs randomy seect resource bocks for direct data transmission without signaing enb, with CDM used to resove the coision probem. Under the proposed architecture, however, the data transmission is inherenty nonorthogona and the enb has to detect the data without any information from UEs, making the receiver too compex to impement. To ease the burden of the receiver, we design a pseudorandom resource seection scheme such that the search space of the data detection is reduced. The anaysis and simuation resuts show the advantage of using the proposed direct access scheme to improve the efficiency of sma data transmission in ceuar networks. I. INTRODUCTION In current LTE networks, a user equipment (UE) acquires UEspecific resources for transmission by a fourstep random access procedure [1]. First, the UE transmits a random access preambe generated by a ZadoffChu (ZC) sequence on the upink random access channe (RACH). The transmission of the random access preambe is used to inform the enb the attempt of random access. Then, the UE waits for the random access response (RAR). Once the enb decodes the preambe successfuy, the enb repies an RAR containing preambe identifier, initia upink grant, and the assignment of the ce radio network temporary identifier (CRNTI) on the downink shared channe (DLSCH). After receiving the RAR, the UE conveys connection request with the UE identifier on the upink shared channe (ULSCH). Finay, the contention resoution is sent by the enb to inform the UE that the connection is estabished. When sma amount of data is transmitted, such as in the scenarios of machinetype communications (MTC) [2], the efficiency probem arises due to signaing overhead. The extensive signaing between UE and enb foowing the fourstep random access procedure for transmitting sma amount of data is extremey inefficient. To achieve ow signaing overhead for sma data transmissions in ceuar networks, we propose a direct access scheme in this paper. UEs transmit data on the resource bocks (RBs) seected randomy, instead of on the RBs instructed by the enb via the fourstep random access procedure. Due to the inherent nonorthogona transmissions, a coision may happen when there are more than one UE that use the same RBs. To cope with the coision probem, code division mutipexing (CDM) is used on top of the singecarrier frequencydivision mutipe access (SCFDMA) to form SCFDMACDM for aowing different UEs to share the same resources by choosing different spreading codes. CDM not ony prevents different UEs from coision but aso provides spreading gain, aowing using higher channe code rate with negigibe performance degradation. Nevertheess, without any signaing between UE and enb under the proposed architecture, in which the resources used by UEs are unknown to the enb, the enb has to try a possibe combinations of RBs and spreading codes (due to CDM) in order to detect the data, incurring a significant receiver burden. To reduce the receiver compexity, a pseudorandom resource seection scheme is proposed. The UE chooses the resource bocks and spreading codes for transmission using the mapping tabe generated according to a UEspecific pseudorandom mapping. The proposed scheme significanty reduces the compexity with negigibe performance oss, as proved by the anaytica and simuation resuts. There exist some works that try to reduce the signaing overhead caused by the random access procedure. In a paper by Zhou et a. [3], a contentionbased random access scheme is proposed. UEs transmit data on the RBs seected randomy, and the information about which resource bocks are seected by the UEs is transmitted on the physica upink contro channe (UCCH). Then, the enb preserves resources to perform a coisionfree access for the coided UEs. Athough their scheme is (semi)contentionbased, signaing exchange between UE and enb is sti required. In a paper by aiva et a. [4], devices which need to access enb periodicay avoid the random access procedure by informing the enb the next time(s) when they need a connection. Athough their proposed scheme reduces the signaing overhead, it ony appies to stationary devices with periodic transmissions. In a paper by Bas et a., binary BCH codes are optimized for sma data transmissions [5], but their proposed scheme does not reduce signaing overhead. II. SCFDMACDM As shown in Fig. 1, the proposed SCFDMACDM system is buit on top of the traditiona LTE upink network [6], [7], [8], [9], [1] by incorporating CDM into the SC
2 Fig. 2. seection. roposed transmitter architecture with pseudorandom resource Fig. 1. Bock diagram of the LTE upink shared channe (ULSCH) and the proposed SCFDMACDM physica upink shared channe (USCH). The grayed bock is the new functiona bock in SCFDMACDM that does not appear in the traditiona LTE USCH. FDMA system. The spreading matrix is defined as S L = [s [,...,s i,...,s L 1 ] T, where L ] is the spreading factor, s i = 2π 1 i 2π (L 1) i j 1,e L j,...,e L and ( ) T means transpose. Under the proposed system, the number of occupied RBs is increased by the spreading factor L. To achieve spectrum efficiency, we et the SCFDMACDM signas occupy the same number of RBs with the same number of data bits as in the conventiona SCFDMA system by increasing the channe code rate by L. Thus, given a channe code rate R in the SCFDMA system, the corresponding channe code rate in the SCFDMACDM system with the spreading factor L is L R. III. SEUDORANDOM RESOURCE SELECTION The proposed direct access scheme for removing the signaing procedure in the conventiona random access reies on the UEs to perform data transmission on the randomy seected resource bocks with randomy seected spreading codes. Nevertheess, the combination of choices of RBs and spreading codes produces a arge search space and yieds high receiver compexity. A simpe soution is to et UEs choose a segment of continuous RBs for transmission and determine the spreading codes according to some predetermined rues to reduce the search space. However, using continuous RBs may cause high coision probabiity and resut in poor system performance, as wi be shown ater. As a resut, we propose a resource seection scheme which pseudoy randomizes the utiization of RBs and spreading codes such that the search space is sma compared to the random seection approach and the system performance is much better than the continuous seection scheme. A. Transmitter Design The transmitter architecture for the pseudorandom resource seection is iustrated in Fig. 2. The goa is to randomize both the RB utiization and the spreading code seection. To randomize the RB utiization, a UE needs first to choose a cycic shift vaue α N randomy from to α max (the defaut vaue of α max is the number of subcarriers minus one, e.g., 11) as a parameter for generating the reference signa and the RB mapping tabe. The mapping tabe is generated by using a UEspecific mapping matrix. Let B be the number Fig. 3. Exampe of the generation of resource bock mapping tabe. α =1, B =15, and the size of the mapping matrix is 3 5 (N v =3and M v =5). of resource bocks. The set of ordered RB index (from the smaest to the argest) I = {1, 2,,B} is shifted circuary according to the cycic shift vaue α chosen by the UE and becomes I α. The indices are then written rowwise into a mapping matrix v V with size N v M v chosen randomy from a set of matrices V. If the number of entries of the mapping matrix is arger than the tota number of avaiabe RBs, dummy zeros are appended to the input. For exampe, if the ordered indices [1, 2,...,15] are appied with a 4 4 matrix, the sequence to be written into the mapping matrix is [, 1, 2,...,15]. Fig. 3 iustrates an exampe of the generation of the resource bock mapping tabe. If UE chooses 1 as the cycic shift vaue, i.e., α = 1, then the shifted indices become [15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 11, 12, 13, 14]. The shifted indices are written into a 3 5 matrix rowwise and read out coumnwise, resuting in a resource bock mapping tabe [15, 5, 1, 1, 6, 11, 2, 7, 12, 3, 8, 13, 4, 9, 14]. In order to further increase the randomness of the resource pattern, the writein order at even rows of the mapping matrix may be atered according to the row cycic shift vaue u determined by the UE. For exampe, the writein order [6, 7, 8, 9, 1] may be changed to [7, 8, 9, 1, 6] in the second row. After this step, the RB mapping resut I α,v,u is obtained by reading out the indices from the mapping matrix coumnwise. The dummy zeros are ignored during the readout. After the resource bock mapping tabe is constructed, the UE picks a number τ (virtua index) such that a segment of continuous RBs with a ength of K indices starting from the τth eement of I α,v,u is chosen. Let the coection of the τth to the (τ +K 1)th eements of I α,v,u be denoted as I α,v,u,τ,k. The RBs indexed by I α,v,u,τ,k (physica index) are used for data transmission. For exampe, if α =1, v is a 3 5 matrix, u =, τ =4, and K =6, then I 1,v,,4,6 =[1, 6, 11, 2, 7, 12].
3 Fig. 4. roposed receiver architecture for the pseudorandom resource seection scheme. The demoduation reference signa (DMRS) used in the conventiona upink, however, is not suitabe for SCFDMA CDM. In the conventiona SCFDMA, the ength of DMRS is equa to the number of occupied RBs. For exampe, if there are 3 RBs, the ength of DMRS is 36. However, in the proposed scheme, due to the remova of signaing procedures, there is no information about the parameters used by the UE for the enb to determine the reference signa. Moreover, different UEs may occupy the same resource bocks and corrupt the orthogonaity of reference signas. Therefore, the reference signas are designed to be bunded with the resource bock seection in the proposed SCFDMACDM system: reference signas are right (circuary) shifted α within the RB if α is chosen in the resource bock seection. To randomize the spreading code seection, we propose to use the RB index I α,v,u,τ,k, the entry number of RB indices, and the cycic shift α to generate the code index C i of the ith corresponding RB with the formua C i = mod(i α,v,u,τ,k [i]+i + α, L), i =1, 2,...,K. (1) For exampe, assume I α,v,u,τ,k =[1, 6, 11, 2, 7, 12] and α = 1. Then, the spreading code pattern can be derived as C = mod([1, 6, 11, 2, 7, 12] + [1, 2, 3, 4, 5, 6] + [1, 1, 1, 1, 1, 1], 3) = [,,, 1, 1, 1], which corresponds to using spreading codes s, s, s, s 1, s 1, and s 1, respectivey. Data is then transmitted on the RBs using the seected spreading code pattern through the upink physica ayer (ULHY) SCFDMACDM signa processing chain. B. Receiver Design At the receiver side, as shown in Fig. 4, the received signa first goes through the reference signa detection, which checks the RBs with every known reference signa pattern to indicate which cycic shift α is used and whether an RB is used for data transmission or not. The receiver first assumes an α vaue and starts decoding the data. A α vaues are then tried in order to infer by which combinations the decoded data can pass the CRC. Mathematicay, by fixing α, the detector output Q α,i of the ith RB is Q α,i = α max k= r i[k]xα[k], where r i is the received reference signa at the ith RB, X α is the known reference signa with cycic shift α, and * means conjugate. Q α,i is normaized to Q α,i and then compared to a threshod γ for a RBs. Let J α be the coection of RB indices that Q α,i γ (γ is set to.5 in this paper). The receiver then finds the continuous segment of RB indices resuted from the cycic shift α, the mapping matrix v, and the row cycic Fig. 5. Exampe of the derivation of τ. shift u that match J α (but maybe in different order). That is, τ =argmax τ I α,v,u,τ,k J α, where the operation searches for the common eements and the operation counts the number of eements. After this step, the estimated index τ indicating the starting point of the continuous segment of virtua indices can be found. Fig. 5 iustrates how the vaue τ is derived using the exampe in Fig. 3, assuming no noise and equa received power from both UEs. The reference signas from two different UEs are overapped on some RBs, and the receiver detects signas on RBs 1, 2, 3, 6, 7, 11, and 12, i.e., J α =[1, 2, 3, 6, 7, 11, 12]. One can easiy check that the two τ vaues that match this detection resut are τ =4and τ =5, with the former τ corresponds to [1, 6, 11, 2, 7, 12] and the atter corresponds to [6, 11, 2, 7, 12, 3]. Now the spreading codes can be derived since the cycic shift α is assumed, the mapping matrix is known, and the index τ can be estimated. Then the receiver can decode the data and check whether the decoded data passes the CRC. By trying a the combinations of cycic shift vaue α, mapping matrix v, and row cycic shift u, the UE data can be decoded. C. arae Interference Canceation (IC) Since the interference may deteriorate the system performance, parae interference canceation can be appied. The timedomain received signa r is first converted to the frequencydomain signa by performing DFT after removing the cycic prefix. During the th iteration, the receiver appies the same procedure as at the transmitter side to the decoded bits of UE m from the ( 1)th iteration in order to ˆb ( 1) m generate the CDMspread signa ˆ D() m. The regenerated CDMspread signa is then passed through the channe H m, which is assumed known. The interference from UE m can then be ˆb () i estimated as I m () = H ˆ D() m m. The new data bits for UE i are obtained by passing the interferencecanceed signas r () i = r i M m=1 m i I() m, i =1,...,M to the detector, where M represents the number of UEs. Note that in the proposed receiver (Fig. 4), the IC is optiona. IV. ANALYSIS OF COLLISION ROBABILITY AND COMLEXITY In this section, we derive the coision probabiity and compare the compexity of different schemes. The foowing three cases are considered: (a) : Both RBs and spreading codes are seected randomy, (b) : RBs
4 Fig. 6. Exampe of RB distribution for B =6, M =4, and K =3. are determined by seecting a segment of continuous RBs, and spreading codes are determined by a predetermined rue, and (c) seudorandom: RBs are determined by a UEspecific mapping matrix, and spreading codes are determined by a predetermined rue. This is the proposed scheme. A. Anaysis of Coision robabiity A coision happens when different UEs occupy the same RB with the same spreading code used. We begin with anayzing the coision probabiity of the random resource seection scheme. We seect one UE as the UE of interest (UOI) and discuss the coision scenarios caused by the remaining M 1 UEs. First, et us define the RB coision indication matrix R with entries being r i,j,i =1,...,M 1,j =1,...,K, and the RB coision number vector d =[d 1,d 2,...,d K ], where d q = M 1 p=1 r pq, 1 q K (i.e., d q indicates the number of coided RBs on the qth RB of UOI). Fig. 6 iustrates an exampe of RB distribution for six avaiabe RBs, four UEs, and three RB utiization per UE, i.e., B =6, M =4, and K =3. By assuming that UE 1 is the UOI and there are respectivey three and two RBs coiding with the second and third RBs of UE 1, as shown in Fig 6, then the RB coision indication matrix is R = and the RB coision 1 1 number vector is d =[, 3, 2]. The number of ( possibe RB ) coision number vectors, N d,is M + K 1 given by N d =. Since different permutations K of R may have the same d, we denote the RB coision number vector and the RB coision indication matrix respectivey as d (i) and R (j,i) where 1 i N d and 1 j N (i) N (i) R R ; is the number of permutations of the RB coision indication [ matrices corresponding ] to d (i). Denote t (j,i) = t (j,i) 1,t (j,i) 2,...,t (j,i) M 1 as the number of 1 s in each row of R (j,i), i.e., the tota number of coided RBs with the UOI for each UE. Then, the number of RB combinations corresponding to d (i) is derived as ( )( N (i) B K CR = K χ ) N (i) R j=1 M 1 =1 ( ) B K K t (j,i) N (j,i), (2) where χ is the number of RBs used by the UOI that are coided, and N (j,i) is the number of different permutations of R (j,i). In (2), χ can be computed as χ = G r (F rz ((d (i) ) T )), where the operator F rz ( ) is to remove the zeros in the vector and the operator G r ( ) is to count the number of rows in a matrix. Let d (i) = F rz ((d (i) ( ) T ) and R (j,i) = F ) ur R (j,i), where the operator F ur ( ) is to output the unique rows in a matrix. Then, N (j,i) can be computed as N (j,i) χ! (M 1)! = ( ) ω G s d (i)t,d ω (i) ( ),! ζ G s R (j,i), r (j,i) ζ! (3) where d (i) ω are the entries of d (i), r (j,i) ζ are the row vectors of R (j,i), and the operator G s (A, v) is to count the number of row vectors of matrix A that is equa to the vector v. Now, we consider the coision of spreading codes. From d, we construct a code coision indication matrix Z with entries being z i,j,i=1,...,n z,j =1,...,K, whose N z row vectors represent the possibe code coision scenarios under the RB coision condition indicated by d. For the exampe described in Fig. 6, the code coision indication matrix is given by Z with the row vectors [ 1 1], [ 1 ], [ 1], and []representing the four possibe code coision scenarios due to the RB coisions indicated by d =[, 3, 2]. Let ξ (i) be the number of ones in the th coumn of R (i) and η = K =1 z(i) q be the number of code coisions. Note that R (j,i) is simpified to R (i) because R (j,i) j have the same code coision indication matrix. The number of code combinations Ξ (i) q,,η according to the RB coision condition indicated by the th coumn of R (i) and the code coision condition indicated by the qth row of Z (i) can be derived as L M, if d (i) =, z (i) q =, (L 1) ξ(i) L M ξ(i), if d (i) >, z (i) q =, Ξ (i) q,,η = ξ (i) [( ξ (i) j ) (L 1) ξ(i) j j=1 ] L M ξ(i), if d (i) >, z (i) q =1. (4) Therefore, the probabiity of having η spreading codes coided can be computed as η = i Q N (i) CR q Q K =1 Ξ(i) q,,η [( B K ) L K ] M, (5) { where Q = (i, q) G r (F rz ((d (i) ) T )) η, } K =1 z(i) q = η. For the continuous and pseudorandom seection schemes, it is difficut not ony to write down a cosed form but aso to perform computerbased simuations due to the arge search space, in particuar for the pseudorandom seection scheme. Therefore, we turn to investigate the access probabiity. Fig. 7 shows the simuation resuts of access probabiity for different RB indices. The simuation parameters are: the number of UEs M is 6, the tota number of avaiabe RBs B is 15, the number of RBs occupied per UE K is 6, the spreading factor L is 3, the number of avaiabe cycic shifts for the spreading code seection N s = α max +1 is 12, and the mapping matrices V are {3 5, 4 4, 5 3}. In Fig. 7, we can find that both the access probabiities of the random and pseudorandom
5 Access probabiity RB index Size of search space Number of RBs occupied per UE (a) Fig. 7. Access probabiity of different resource seection schemes robabiity (ana.), (approx.) (ana.) (sim.) (sim.) (sim.) Fig. 9. Size of search space Tota number of avaiabe RBs (b) Comparison of compexity for different resource seection schemes Number of coisions Fig. 8. Anaytica and simuated coision probabiities of different resource seection schemes. Note that the anaytica resut of the random seection scheme is used to approximate the proposed pseudorandom seection scheme. seection schemes are uniformy distributed, but the access probabiity of the continuous seection scheme varies with the RB index. Therefore, it can be expected that the anaytica resut of the random seection scheme can approximate the resut of the pseudorandom seection scheme. Fig. 8 compares the anaytica and simuation resuts of coision probabiity for different resource seection schemes. It indeed shows that the coision probabiity of the pseudorandom seection scheme can be approximated by the random seection scheme. It aso shows that the continuous seection scheme, which has the owest compexity, has a high probabiity of zero coision but aso has a high probabiity of six coisions (note that the number of RBs occupied per UE is six). This is why the continuous seection scheme performs worse, as shown ater. B. Compexity Anaysis To understand how much detection compexity can be saved using the proposed pseudorandom resource seection scheme, we compare the compexity in this subsection. The detection compexity is measured by the size of the search space, which is the number of the combinations of RBs and spreading codes. The detection compexity of the random seection scheme is given by considering randomy choosing K ordered RBs from B avaiabe RBs and randomy picking a spreading code for B! each chosen RB, i.e., K! (B K)! K! LK = B! LK (B K)!. For the continuous seection scheme, the spreading code is determined according to (1) and the compexity is (B K +1) N s, where (B K +1) is the number of possibe continuous segments. For the pseudorandom seection scheme, we take the number of avaiabe mapping matrices V (the cardinaity of V) and the row cycic shift operation into account. The detection compexity of the pseudorandom seection scheme is (B K+1) N s β, where β is the tota number of coumns of avaiabe mapping matrices, i.e., β = V =1 N co(); N co is the number of coumns of the mapping matrix chosen by UE. Fig. 9(a) compares the compexity of the three schemes using the same parameters as we evauate the coision, except that the number of coumns of matrix N co is 5 and we ony consider one mapping matrix with size 3 5. When the number of RBs used by each UE is arge, the detection compexity of the random seection scheme is prohibitivey high whie the compexity of the proposed pseudorandom seection scheme stays at a moderate eve. The same situation appies when different number of RBs are used, as shown in Fig. 9(b). V. ERFORMANCE EVALUATION In this section, we present the performance evauation of the proposed system. We first consider the effect due to CDM and then investigate the performance of the proposed pseudorandom resource seection scheme under the EVehA mutipath channe. The performance is evauated by the bock error
6 BLER Throughput (%) UE w/o CDM (CR =.119), C 2 UEs w/ CDM (CR =.238), C 3 UEs w/ CDM (CR =.357), C 1 UE w/o CDM (CR =.119), EC 2 UEs w/ CDM (CR =.238), EC 3 UEs w/ CDM (CR =.357), EC 1 UE w/o CDM (CR =.238), C 1 UE w/o CDM (CR =.357), C (a) Bock error rate 1 UE w/o CDM (CR =.119), C 2 UEs w/ CDM (CR =.238), C 3 UEs w/ CDM (CR =.357), C 1 UE w/o CDM (CR =.119), EC 2 UEs w/ CDM (CR =.238), EC 3 UEs w/ CDM (CR =.357), EC 1 UE w/o CDM (CR =.238), C 1 UE w/o CDM (CR =.357), C (b) Throughput Fig. 1. erformance of using CDM under the EVehA mutipath channe. CR, C, and EC respectivey represent code rate, perfect channe estimation, and estimated channe. rate (BLER) and the throughput (in percentage). The 1% throughput is defined as the throughput which can be achieved by a singe UE since our goa is to increase the throughput aong with the increase in the number of UEs. As a resut, it is possibe that the resuting throughput is more than 1%. A. Effect of CDM First, we compare the performances of the systems with and without using CDM. The RB usage and spreading codes are assumed known by the receiver. The EVehA channe with speed 3 km/hr is chosen for demonstration. The simuation parameters are the foowing. Bandwidth is set to 1.4 MHz, moduation is QSK, the number of RBs per UE is 6, data is 152 bits ong, the size of DFT is 72, the size of IDFT is 128, and the spreading factors are 2 (for 2 UEs) and 3 (for 3 UEs). Three cases are considered: singe UE without CDM, 2 UEs with CDM, and 3 UEs with CDM. The bock error rates are compared in Fig. 1(a). When the same channe code rate is appied, the system with CDM outperforms the system without CDM due to the spreading BLER BLER with IC with IC with IC (a) 7 UEs with IC with IC with IC (b) 2 UEs Fig. 11. erformance comparison of different resource seection schemes with and without IC under the EVehA mutipath channe. gain. It can be found that the performance degradation due to the higher channe code rate is compensated by the spreading gain. However, the case of 3 UEs with the channe code rate.357 suffers sight performance degradation due to the mutipath effect. To understand how channe estimation affects the system performance, we aso compare the BLER performances of the case when the channe is perfecty known and of the case when the frequency domain east square is used for channe estimation and the minimum mean square error (MMSE) criterion is used for equaization. In Fig. 1(a), we observe that the imperfect channe estimation degrades the system BLER and resuts in error foors. However, the BLER is sti in the usabe range. The throughput performances are compared in Fig. 1(b). It is shown that, even under the nonperfect channe estimation, more than 1% throughput performance can be achieved (note again that the 1% throughput is defined as the throughput which can be achieved by a singe UE) and the throughput scaes amost ineary with the number of UEs.
7 BLER Throughput (%) UEs () 1 UEs () 2 UEs () 7 UEs () 1 UEs () 2 UEs () 7 UEs () 1 UEs () 2 UEs () (a) Bock error rate 7 UEs () 1 UEs () 2 UEs () 7 UEs () 1 UEs () 2 UEs () 7 UEs () 1 UEs () 2 UEs () 1 UEs 2 UEs 7 UEs (b) Throughput Fig. 12. erformance comparison of different resource seection schemes under the EVehA mutipath channe. erfect channe estimation is assumed. B. erformance of seudorandom Resource Seection Now we present the performance of the proposed pseudorandom resource seection scheme. The EVehA channe with user speed 3 km/hr is again chosen for demonstration. Bandwidth is set to 1 MHz, moduation is QSK, the number of RBs per UE is 6, data is 152 bits ong, the spreading factor is 3, and the sizes of mapping matrices are 5 1, 6 9, and 1 5. Fig. 11 shows the performance with and without IC for different number of UEs. When no IC is appied, a schemes perform worse due to interference; when IC is appied, both the random and the proposed pseudorandom seection schemes outperform the continuous seection scheme. Fig. 12(a) and Fig. 12(b) compare the BLER and the throughput performance respectivey. erfect channe estimation is assumed. It is shown that the random seection scheme provides the best performance. This is expected since it has the argest compexity. In the cases of 7 UEs and 1 UEs, the continuous seection scheme provides acceptabe BLER performance. However, for the case of 2 UEs which corresponds to the 8% LTE system oading, using the continuous seection scheme cannot achieve the.1 BLER requirement specified by LTE. On the other hand, the pseudorandom seection scheme not ony outperforms the continuous seection scheme in a cases, but aso provides a performance very cose to the random seection scheme in terms of throughput. Remember that the compexity of the proposed pseudorandom seection scheme is much ower than the random seection scheme. This demonstrates the superiority of the proposed pseudorandom resource seection scheme. VI. CONCLUSIONS In this paper we have proposed a direct access system with pseudorandom resource seection for sma data transmission in ceuar networks. Code division mutipexing has been introduced on top of the conventiona SCFDMA system to aow the same resources to be shared among different UEs. UEs seect RBs and spreading codes for data transmission without signaing enb in order to reduce overhead. With the proposed pseudorandom resource seection scheme, the receiver compexity can be reduced significanty compared to the random seection of resource bocks and spreading codes. The anaytica and simuation resuts have shown that the performance of the proposed pseudorandom resource seection scheme is cose to that of the random seection scheme with much ower compexity, and demonstrated the feasibiity of the proposed CDMbased system for direct access in ceuar networks. ACKNOWLEDGEMENT The support from Industria Technoogy Research Institute (ITRI) and Nationa Science Counci (NSC), Taiwan (under grant MOST E12) is gratefuy acknowedged. REFERENCES [1] 3G TS V1.., Evoved universa terrestria radio access (EUTRA) medium access contro (MAC) protoco specification, Dec. 21. [2] 3G TR V11.., Study on RAN improvements for machinetype communications, Sept [3] K. Zhou, N. Nikaein, R. Knopp, and C. Bonnet, Contention based access for machinetype communications over LTE, in roc. IEEE Vehicuar Technoogy Conference (VTC Spring), May 212, pp [4] R. aiva, H. Wihem, M. Saiy, D. Navrati, and M. Taponen, Overoad contro method for synchronized MTC traffic in GERAN, in roc. IEEE Vehicuar Technoogy Conference (VTC Fa), Sept. 211, pp [5] J. Bas, F. VazquezGaego, C. Gavrincea, and J. AonsoZarate, Energy and deay anaysis of binary BCH codes for machinetomachine networks with sma data transmissions, in roc. IEEE IMRC, Sept. 213, pp [6] 3G TR V1.5., Mutipexing and channe coding, Mar [7] 3G TS V1.1., Evoved universa terrestria radio access (EUTRA) physica channes and moduation, Mar [8] E. Dahman, S. arkva, and J. Skod, 4G: LTE/LTEAdvanced for Mobie Broadband. Esevier, May 211. [9] S. Sesia, I. Toufik, and M. Baker, LTE  The UMTS Long Term Evoution: From Theory to ractice, 2nd ed. Wiey, Juy 211. [1] J. Bumenstein, J. C. Ikuno, J. rokopec, and M. Rupp, Simuating the ong term evoution upink physica ayer, in roc. Internationa Symposium ELMAR, Zadar, Croatia, 211.
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