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IEEE ICC 2015 - Wireless Communications Symposium Iterative Multiuser Receiver in Sparse Code Multiple Access Systems Yiqun Wu, Shunqing Zhang, and Yan Chen Huawei Technologies, Co. Ltd., Shanghai, China Email: {wuyiqun}@huawei.com Abstract-Sparse code multiple access (SCMA) is a novel non- orthogonal multiple access scheme, in which multiple users access the same channel with user-specific sparse codewords. In this paper, we consider an uplink SCMA system employing channel coding, and develop an iterative multiuser receiver which fully utilizes the diversity gain and coding gain in the system. The simulation results demonstrate the superiority of the proposed iterative receiver over the non-iterative one, and the performance gain increases with the system load. It is also shown that SCMA can work well in highly overloaded scenario, and the link-level performance does not degrade even if the load is as high as 300%. I. INTRODUCTION To satisfy the demand of high spectral efficiency and mas- sive connections in next generation cunication systems, non-orthogonal multiple access schemes have received more and more attention [ 1]. One important reason is that non- orthogonal multiple access can theoretically expand the capac- ity region [2]. A familiar example of non-orthogonal multiple access is code division multiple access (CDMA), which has been deeply investigated and successfully applied. However, the optimal multiuser detection in CDMA systems is of high complexity, which increases exponentially with the number of users [3]. Although many low-complexity receivers have been proposed, they usually perform worse than the optimal one, especially when the system is overloaded, i.e., the number of users is larger than the number of chips [4]. Low density spreading (LDS) has been proposed to reduce the complexity of multiuser detection [5]-[8]. In the LDS system, modulated symbols are only spread over a part of total chips and the signatures on other chips are zero. Therefore, the number of interfering users on each chip is much lower than the traditional CDMA. Similar to low-density parity- check (LDPC) code, LDS signatures can be represented by a sparse factor graph [9], with variable nodes (VNs) representing data symbols and function nodes (FNs) representing chips. By taking advantage of the sparsity, message passing algorithm can be applied for multiuser detection, which has much lower complexity than optimal maximum a posteriori (MAP) decoder but achieving almost the same performance. Sparse code multiple access (SCMA) was introduced in [10], which generalizes the idea of LDS. In the SCMA system, the QAM mapper and the symbol spreader are combined into a single block of SCMA encoder, which maps a group of bits to multidimensional complex domain codewords. Similar to LDS, the signatures of SCMA codewords are sparse and can SC decoder OFDM Demodulator AWGN Fig. 1: Block diagram of an uplink SCMA system. be represented by a sparse factor graph. By carefully designing the factor graph and mapping functions, SCMA can perform better than LDS with similar decoding complexity [ 1 1]. Inspired by the turbo principle [12], iterative multiuser receivers have been investigated in CDMA systems [13], [14] and also in the LDS system [15]. In this paper, we consider an uplink SCMA system employing channel coding, and develop an iterative multiuser receiver for the SCMA system. It will be shown that how the soſt decisions are exchanged between the SCMA decoder and channel decoders to fully utilize the diversity gain and coding gain, and how the decoding complexity can be reduced by exploiting the special structure of SCMA codebook and tailoring the factor graph during the iterations. The simulation results demonstrate the superiority of the proposed iterative receiver over the non-iterative one. The simulation results also show that SCMA can work well in highly overloaded scenario, and the performance will not degrade even if the load is as high as 300%. In this paper, the set of binary and complex numbers are denoted by and C, respectively. We use x, x, and X to represent a scalar, a vector and a matrix. The rest of the paper is organized as follows. Section II introduces the system model. Section III presents the details of the iterative multiuser receiver. In Section IV, the receiver performance is evaluated. Section V concludes the paper. II. SYSTEM MODEL Consider a K -user uplink SCMA system depicted in Fig. 1. For each user k, the binary information data bk are encoded by a channel encoder with coding rate Rk. The coded bits Ck 978-1-4673-6432-4/15/$31.00 ©2015 IEEE 2918
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Page 1: Iterative Multiuser Receiver in Sparse Code Multiple ...b90088/new/Iterative... · v=1 v=lj: cv,j=l In practice, the max-log approximation is applied to reduce the computational complexity:

IEEE ICC 2015 - Wireless Communications Symposium

Iterative Multiuser Receiver in Sparse Code Multiple Access Systems

Yiqun Wu, Shunqing Zhang, and Yan Chen Huawei Technologies, Co. Ltd., Shanghai, China

Email: {wuyiqun}@huawei.com

Abstract-Sparse code multiple access (SCMA) is a novel non­orthogonal multiple access scheme, in which multiple users access the same channel with user-specific sparse codewords. In this paper, we consider an uplink SCMA system employing channel coding, and develop an iterative multiuser receiver which fully utilizes the diversity gain and coding gain in the system. The simulation results demonstrate the superiority of the proposed iterative receiver over the non-iterative one, and the performance gain increases with the system load. It is also shown that SCMA can work well in highly overloaded scenario, and the link-level performance does not degrade even if the load is as high as 300%.

I. INTRODUCTION

To satisfy the demand of high spectral efficiency and mas­sive connections in next generation cOlmnunication systems, non-orthogonal multiple access schemes have received more and more attention [ 1]. One important reason is that non­orthogonal multiple access can theoretically expand the capac­ity region [2]. A familiar example of non-orthogonal multiple access is code division multiple access (CDMA), which has been deeply investigated and successfully applied. However, the optimal multiuser detection in CDMA systems is of high complexity, which increases exponentially with the number of users [3]. Although many low-complexity receivers have been proposed, they usually perform worse than the optimal one, especially when the system is overloaded, i.e., the number of users is larger than the number of chips [4].

Low density spreading (LDS) has been proposed to reduce the complexity of multiuser detection [5]-[8]. In the LDS system, modulated symbols are only spread over a part of total chips and the signatures on other chips are zero. Therefore, the number of interfering users on each chip is much lower than the traditional CDMA. Similar to low-density parity­check (LDPC) code, LDS signatures can be represented by a sparse factor graph [9], with variable nodes (VNs) representing data symbols and function nodes (FNs) representing chips. By taking advantage of the sparsity, message passing algorithm can be applied for multiuser detection, which has much lower complexity than optimal maximum a posteriori (MAP) decoder but achieving almost the same performance.

Sparse code multiple access (SCMA) was introduced in [ 10], which generalizes the idea of LDS. In the SCMA system, the QAM mapper and the symbol spreader are combined into a single block of SCMA encoder, which maps a group of bits to multidimensional complex domain codewords. Similar to LDS, the signatures of SCMA codewords are sparse and can

SCMA decoder

OFDM Demodulator

AWGN

Fig. 1: Block diagram of an uplink SCMA system.

be represented by a sparse factor graph. By carefully designing the factor graph and mapping functions, SCMA can perform better than LDS with similar decoding complexity [11].

Inspired by the turbo principle [ 12], iterative multiuser receivers have been investigated in CDMA systems [13], [14] and also in the LDS system [15]. In this paper, we consider an uplink SCMA system employing channel coding, and develop an iterative multiuser receiver for the SCMA system. It will be shown that how the soft decisions are exchanged between the SCMA decoder and channel decoders to fully utilize the diversity gain and coding gain, and how the decoding complexity can be reduced by exploiting the special structure of SCMA code book and tailoring the factor graph during the iterations. The simulation results demonstrate the superiority of the proposed iterative receiver over the non-iterative one. The simulation results also show that SCMA can work well in highly overloaded scenario, and the performance will not degrade even if the load is as high as 300%.

In this paper, the set of binary and complex numbers are denoted by lR and C, respectively. We use x, x, and X to represent a scalar, a vector and a matrix. The rest of the paper is organized as follows. Section II introduces the system model. Section III presents the details of the iterative multiuser receiver. In Section IV, the receiver performance is evaluated. Section V concludes the paper.

II. SYSTEM MODEL

Consider a K -user uplink SCMA system depicted in Fig. 1. For each user k, the binary information data bk are encoded by a channel encoder with coding rate Rk. The coded bits Ck

978-1-4673-6432-4/15/$31.00 ©2015 IEEE 2918

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IEEE ICC 2015 - Wireless Communications Symposium

o VNs D FNs

Fig. 2: Example of factor graph (V = 12, N = 8).

11 lO

-a

FN 1

01 00

a

11 lO

-a o

FN2

01 00

a

Fig. 3: Illustration of the mapping function of 4-point SCMA code book (M = 4).

are then mapped into complex domain codewords Xk by an SCMA encoder. The signatures of SCMA codewords can be represented by a factor graph Q(V , N), which contains V VNs and N FNs. Fig. 2 shows an example of factor graph, in which V = 12, N = 8. The VNs represent data layers, and the FNs represent resources shared by data layers. The edges between VNs and FNs mean the corresponding data layers have non­zero signatures on the associated resources. Since there are totally V data layers on N resources, define the system load

v as p = N' For each data layer, every J = log2 M coded bits are

mapped to an N-dimensional SCMA codeword. The asso­ciated mapping function for the v-th data layer is defined as: Iv : ]RJ --+ X, where X E eN

and IXI = M. Let Cv = (Cv,l , Cv,2 , " . , cv,J) be the coded bits, and Xv = (Xv,1 ,Xv,2 ,"' ,Xv,N) be the SCMA codeword. Let C = (CI, C2, . . . , cv ) be the coded bits of all data layers. Let 1> (v) = {n : xv,n -I- O} be the neighbor FNs of the VN v, and ll1 (n) = { v : xv,n -I- O} be the neighbor VNs of the FN n.

Each SCMA codeword is a sparse vector with no more than half non-zero elements. The element xv,n is non-zero if and only if there is an edge between the VN v and the FN n in Q. Fig. 3 illustrates the mapping function of the 4-point SCMA codebook, which means M = 4. In this case, each VN has two neighbor FNs and every two coded bits are mapped to an N-dimensional codeword. Only two elements of the codeword are non-zero, which correspond to the two FNs. On the two FNs, the bits 00, 01, 10 and 11 are mapped to (a, 0), (0, a) , (0, -a) , ( -a , 0), respectively.

Each user can have multiple data layers. Let Vk be the set of VNs for the user k, and I Vk I = Vb and 1); ( v) be the user

corresponding to the VN v. For user k, the transmitted signal is the sum of the codewords from all the data layers in Vk:

( 1)

The generated codewords x = {Xl, ... , XK} are then modu­lated by OFDM modulators. Assume each OFDM symbol has Ntotal = SN subcarriers for transmission, which are divided into S subbands and each subband has N subcarriers. Thus, there are S SCMA codewords for each user per layer per OFDM symbol. Let hk,n(i , s) be the channel gain between the user k and the base station (BS) on the n-th subcarrier of the s-th subband of the i-th OFDM symbol, then the received signal on the same subcarrier is given by

K

Yn(i , s) = L hk,n(i , s)xk,n(i , s) + vn(i , s), (2) k=l

where Vn (i , s) is additive Gaussian noise with power 0'2. As the processing procedure is the same for each subband and each OFDM symbol, we will drop the index i and s for notational simplicity in the following analysis. Let y = (Yl, Y2 , ... , Y N) be the received signals, and hk = (hk,l , hk,2 , ... , hk,N) be the channel gains between the user k and the BS.

III. ITERATIVE MULTIUSER RECEIVER

In this section, the details of iterative multiuser receiver for SCMA system are presented. The structure of the receiver is shown in Fig. 4, which consists of two types of blocks: a SCMA decoder and K parallel channel decoders, separated by deinterleavers and interleavers. Given the received signal y and channel knowledge h = ( h1, ''' , hK)' the SCMA decoder delivers the soft decision of every coded bit of every data layer, i.e., a posteriori log-likelyhood ratio (LLR), which is given by

A ( .) -1 P{cv,j = 11Y} 1 CV,J - og P{cv,j = Oly}'

By using Bayes' rule, we have

(3)

A ( ) 1 P{yicv,j = 1} 1 P{cvJ' = 1} 1 Cv,j = og + og ' (4) P{yicv,j = O} P{ Cv,j = O} = AI(Cv,j) + A�(Cv,j) ,

In (4), the first term, denoted by Al(Cv,j), represents the extrinsic information by SCMA decoder, and the second term, denoted by A� (cv,j), represents the a priori LLR of Cv,j which is given by the corresponding channel decoder in the previous iteration. For the first iteration, assuming no prior information and A�(Cv,j) = O.

To compute (4), we can apply the relation between C and x. Define

c:'j £ {(Cl,l,'" , Cv,j-l, 1 , Cv,j+l, '" , CV,J) : Cu,i E {O, 1}, (u, i ) -I- (v,j)}. (5)

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IEEE ICC 2015 - Wireless Communications Symposium

y SCMA decoder

Fig. 4: Structure of the iterative multiuser receiver for SCMA system.

Similarly define C;;'j' Let X:j be the set of codewords mapped

from the coded bits in Ct.j' and similarly Xv�j' By using Bayes' rule,

. LCE c+ . P{cIY} A1(�,v) = log

L V,

J P{ I } cE C- . C Y V,J

LXE x+ P{Ylx}P{x} = log

LXE x�J P{Ylx}P{x} (6) V,)

As the noise vector is independent identically distributed and uncorrelated with the codewords, we have

where P {x} is the a priori probability, which is given by

v J 1 P{x} = II II "2 ( 1 + Cv,j tanh ( A�(Cv,j))) . (8) v=1 j=l

Using (2), P{Ynlx} is given by

P{Ynlx} = �(J

exp (-g;;�)) , (9)

where gn(x) llYn - LVEW(n) Xv,nh,,(v) ,nI12. Define

max(x , y) � log(exp(x) + exp(y)), then combine (8) and (9) into (7) and after simplification we have

AI(cv,j) = maxxE x+ (-�2 � gn(x) + L (X)) v" 2(J � n=l

-maxxE x.;j ( - 2�2 t, gn(x) + L (X)) , (10)

where L(x) is the normalized a priori LLR of x: v v

L(x) = L L(xv) = L L A�(Cv,j). (11) v=1 v=lj: cv,j=l

In practice, the max-log approximation is applied to reduce the computational complexity:

max(x , y) � max(x , Y) , (12)

and the performance degradation due to the approximation is small.

The set of LLRs { A1(Cv,j)} is divided into K subsets:

(13)

and the LLRs in Sk is deinterleaved and fed into k-th channel decoder. The computation of a posteriori LLR is similar for the channel decoder, which can be written as

(14)

where A2 (cv,j) represents the extrinsic information by the channel decoder and Xi (cv,j) represents the a priori LLR of Cv,j from the SCMA decoder. The derivation of LLRs for the channel decoder has been investigated in previous work [14] and is not repeated here.

A. Message Passing Algorithm Computing ( 10) with brute-force is of high complexity,

as the size of the set XV�j is on the order of O(MV). Fortunately, message passing algorithm (MPA) can be applied to approximate the optimal SCMA decoder with much lower complexity. The messages exchanged between VNs and FNs are the reliability values of SCMA codewords, which are represented in logarithm domain. As in [ 15], the message sent from the FN n to the VN v is given by

In-tv(xv) = max {gn(x) + L IU-tn(xu)} (15) xu:uEw(n)\v uEw(n)\v If each FN has dj neighbor VNs, the complexity of computing (15) is O(Mdf), which is much lower than O(MV). The message sent from the VN v to the FN n is given by

Iv-tn(xv) = L Il-+v(xv) + L(xv) (16) lE<I>(v)\n

The complexity of computing (16) is much lower than ( 15). The above message exchange will be applied for several

iterations, which is referred to as inner iterations with respect to the outer iterations between the SCMA decoder and channel decoders. For all n E N and v E V, the initial value of

In-tv(xv) and Iv-tn(xv) are 0 and L(xv), respectively. The inner iterations end after a certain number of iterations, and the extrinsic information of the SCMA decoder is given by

A1(Cv,j) = max I(xv) - max I(xv) - A�(Cv,j) , (l7) Xv:Cv,j=l Xv:Cv,j=O

where

I(xv) = L In-tv(xv)· nE<I>(v)

B. Exploiting the Codebook Structure

(18)

By exploiting the structure of SCMA codebook, the decod­ing complexity can be reduced. Without loss of generality, assume the 4-point SCMA code book is applied for all the VNs shown in Fig. 3. In the example shown in Fig. 5, the FN n has three neighbor VNs VI, V2, and V3. With the 4-point SCMA codebook, each VN has four possible codewords, but only three possible symbols on the FN. Denote the codeword

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IEEE ICC 2015 - Wireless Communications Symposium

n Fig. 5: Example of message passing between FNs and VNs.

index of the VN Vi by mi. Let mi = 1, 2, 3, and 4 represent the codewords mapped from coded bits 00, 01, 10, and 11, respectively. Denote the symbol index of the VN Vi on the FN n by 8i' Let 8i = 1, 2, and 3 represent the symbol value is a, 0, and -a, respectively.

The bottleneck of the decoding complexity is computing the ( 15), which requires exhausting all the combinations of codewords from the df neighbor VNs. With the 4-point SCMA codebook, the computational complexity is O( 4dJ). For the FN n in Fig. 5, ( 15) can be rewritten as

In-tv,(mi) = max {gn(ml, m2, m3) + " IVi -tn(mj)}. mjT1" "f;;: . (19)

Let function fn(81, 82 , 83) be the value of gn(ml, m2, m3) when the symbol indexes of the VNs on the FN n are 81, 82, and 83, respectively. In Fig. 5, n is the second FN for VI

and V2, and the first FN for V3. If coded bits of all the three VNs are 00, the symbols are 0, 0, and a for VI, v2, and V3,

respectively, then we have

fn(2, 2 , 1) = gn(I ,I , I). Similarly, fn(81, 82 , 83) can represent all the other possible

gn(ml, m2, m3)' Define function IVi-tn(8i) as following: If the FN n is the first FN of the VN Vi,

IVi-tn(l) = IVi-tn(I), IVi-tn(2) = max(Ivi-tn(2) , Iv;-tn(3)) , IVi-tn(3) = IVi-tn(4),

if the FN n is the second FN of the VN Vi,

IVi-tn( 1) = IVi-tn(2), I Vi-tn (2) = max(Ivi-tn ( 1) , Iv;-tn (4)) , IVi-tn(3) = IVi-tn(3).

Then, ( 19) can be rewritten as

In-tvi (8i) = max{fn(81, 82 , 83) + L 1vj-tn(8j)}. (20) sj't#J j#i To calculate the In-tvi' it also depends on the relation between the FN and the VN. If the FN n is the first FN of the VN Vi,

In-tvi(2) = In-tvi(3) = In-tvi(I) , In-tvi ( 1) = I n-tVi (2) , In-tvi(4) = In-tvi(3),

if the FN n is the second FN of the VN Vi,

In-tv;(I) = In-tv;(4) = In-tvi(2), In-tv; (2) = I n-tVi ( 1) , In-tv; (3) = I n-tVi (3).

Therefore, an equivalent algorithm is provided to compute the message from the FN n to the VN Vi, and the decoding complexity is O(3dJ), which is much lower than that of the previous algorithm, especially when the df is large.

C. Tailoring the Factor Graph As the decoding complexity increases exponentially as the

degrees of FNs increase, reducing the degrees of FNs or the size of the factor graph is another way to reduce the decoding complexity. There are many possible ways to reduce the size of the factor graph. One is to apply interference cancelation. If cyclic redundancy check is applied along with channel coding, as in LTE [16], it is known that whether a user has been correctly decoded after each iteration. If a user is correctly decoded before the maximum number of iterations is reached, factor graph can be tailored. Let T be the set of users which have been correctly decoded, then the VNs in {v : ", (v ) E T} are cut off from the factor graph, so are the associated edges, and the received signal is updated by

Y = Y - L hk 0 Xk, (21) kET

where 0 represents element-wise multiplication. As the de­grees of FNs decrease, the computational complexity is largely reduced.

If there are gaps between power levels from different users, the users with higher power levels can be decoded in the first stage, with the signal from the left users regarded as noise. Then, the users with lower levels will be decoded in the next stage. Interference cancelation is required between the stages. In this case, factor graphs need to be constructed, by only considering the users to be decoded. Due to the limit of space, the details of the construction are not provided here.

IV. PERFORMANCE EVALUATION

In this section, the performance of the proposed iterative multiuser receiver is evaluated. In an uplink SCMA system, the signatures of SCMA codewords can be represented by a factor graph with 24 VNs and 8 FNs, and the generation matrix is given by (25). The mapping function is shown in Fig. 3, which means M = 4. Assume each user has 2 data layers, thus the system can accommodate 12 users at most. The number of active users varies in the simulation. If there are K active users, the system load is K j 4. All the users have the same average signal to noise ratio (SNR), which is defined as SNR=2a2 ja

2. The fading channel model follows the SCME

urban micro-cell channel model [ 16], and the moving speed of users is 3 kmlh. Assume there is no channel estimation error. The bandwidth is fixed to be 6 resource blocks (RBs). Turbo code is applied as the channel code, and code rate is 112. The simulation results are averaged over 10,000 subframes. If the

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IEEE ICC 2015 - Wireless Communications Symposium

1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0

M24,8 = 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0

Fig. 6: BLER performance with different number of outer loop iterations: 4 active users.

number of active users is less than 12, the active users are selected in a round-robin way. MPA algorithm with the max­log approximation in ( 12) is applied for the SCMA decoder, and the number of inner-loop iterations is 4.

First, we evaluate the performance with different number of outer loop iterations. Fig. 6 and Fig. 7 show the BLER performance when the number of active users is 4 and 12, respectively. The curves confirm the convergence of the it­erative receiver. It shows that the performance gain becomes marginal when the number of outer-loop iterations is larger than 3 for both case. Compared with the non-iterative receiver, the performance gain is about 0.3 dB and 2 dB in SNR at the BLER of 10-2 with 4 iterations, which means the performance gain increases as the system load increases. The above results show that the number of iterations leverages the tradeoff between computational complexity and system performance. In practice, the number of iterations can be adaptive to the system load.

When the number of outer-loop iterations is fixed to be 4, and the BLER performance under different system load is shown in Fig. 8, in which there are four curves: single user, 100% load, 200% load and 300% load. The curve of single user means there is only one active user, and no interference exists. According the definition in Section II, the system load is 100%, 200%, and 300%, when there are 4, 8, and 12 active users, respectively. The results show that the BLER

0 0 1 0 0 0 1 0

0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 1 0 0

(25) 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0

Fig. 7: BLER performance with different number of outer loop iterations: 12 active users.

Fig. 8: BLER performance under different system load (4 outer loop iterations).

performance does not degrade much when the system load increases, even if the load is as high as 300%. The curves almost overlap with each other when the BLER is less than 10-2. In other word, this means SCMA can acconunodate dynamic load changes.

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IEEE ICC 2015 - Wireless Communications Symposium

V. CONCLUSION

In this paper, an iterative multiuser receiver is developed for the uplink SCMA system, which can fully utilize the diversity gain and coding gain. Simulation results show that the iterative receiver performs much better than the non-iterative one, especially when the system load is high. With the iterative receiver, the link-level performance does not degrade much when the system load increases, even under the 300% system load. This means SCMA is a good candidate multiple access scheme in the case of massive connections, which may happen in next generation communication systems.

REFERENCES

[l] Y. Saito, etc., "Non-orthogonal multiple access (NOMA) for future radio access," Proceedings of IEEE VTC-Spring, May, 2013.

[2] o. Tse, and P. Viswanath, F undamentals of wireless communications, Cambridge, 2005.

[3] S. Verdu, "Minimum probability of error asynchronous gaussian multiple access channels," IEEE Trans. Info. Theory, vol. 32, pp. 85-94, Jan. 1986

[4] A. Kapur, and M. K. Varanasi, "Multiuser detection for overloaded COMA systems," IEEE Trans. on Info. Theory, vol. 49, no. 7, pp. 1728-1742, July, 2003.

[5] J. Choi, "Low density spreading for multicarrier systems," Proceedings of IEEE ISSSTA, pp. 575-578, Aug. 2004.

[6] O. Guo, and C. Wang, "Multiuser detection of sparsely spread COMA," IEEE JSAC, vol. 26. no. 3, pp. 421-431, Apr. 2008.

[7] J. van de Beek, and B. M. Popovic, "Multiple access with low-density signatures," Proceedings of IEEE Globecom, Oec. 2009.

[8] R. Hoshyar, F. P. Wathan, and R. Tafazolli, "Novel low-density signature for synchronous COMA systems over AWGN channel," IEEE Trans. on Signal Processing, vol. 56, no. 4, Apr. 2008.

[9] F. Kschischang, B. Frey, and H. Loeliger, "Factor graphs and the sum­product algorithm," IEEE Trans. on Info. Theory, vol. 47. no. 2, pp 498-519, Feb. 2001.

[10] H. Nikopour, and H. Baligh, "Sparce code multiple access," Proceedings

of IEEE PlMRC, Sep. 2013. [11] M. Taherzadeh, H. Nikopour, A. Bayesteh, and H. Baligh, "SCMA

codebook design," Proceedings of IEEE VTC'Fall, Sep. 2014. [l2] J. Hagenauer, "The turbo principle: Tutorial introduction and state of

the art," in Proc. International Symposium on Turbo Codeds and Related Topics, Sept. 1997.

[l3] S. Kaiser, "OFOM code-division multiplexing in fading channels," IEEE Trans. on Communications, vol. 50, no. 8, pp. 1266-1273, Aug. 2002.

[l4] X. Wang, and V. Poor, "Iterative soft interference cancellation and decoding for coded COMA," IEEE Trans. on Communications, pp. 1049-1061, Jul. 1999.

[l5] R. Razavi, et aI., "On receiver design for uplink low density signature OFOM" in IEEE Trans. on Communications, vol. 60. no. II, pp. 3499-3508, Nov., 2012.

[l6] 3GPP TR 36.814, Evolved Universal Terrestrial Radio Access (E­UTRA): Further advancements for E-UTRA physical layer aspects, Mar. 2010.

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