Uplink Sparse Code Multiple Access Research Articles

Asynchronous multiuser detection for sparse code multiple access with unknown delays: A compressed sensing approach

SummaryDespite being the subject of a growing body of research, nonorthogonal multiple access has failed to garner sufficient support to be included in modern standards. One of the more promising approaches to nonorthogonal multiple access is sparse code multiple access, which seeks to utilize nonorthogonal, sparse spreading codes to share bandwidth among users more efficiently than traditional orthogonal methods. Nearly all of the studies regarding sparse code multiple access assume synchronization at the receiver, which may not always be a practical assumption. In this work, we aim to bring this promising technology closer to a practical realization by dropping the assumption of synchronization. We therefore propose a compressed sensing‐based delay estimation technique developed specifically for an uplink sparse code multiple access system. The proposed technique can be used with nearly all of the numerous decoding algorithms proposed in the existing literature, including the popular message passing approach. Furthermore, we derive a theoretical bound regarding the recovery performance of the proposed technique and use simulations to demonstrate its viability in a practical uplink system.

Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System based on Complex-Valued Sparse Autoencoder

Channel estimation is one of the most important aspects of wireless communication. Especially in Sparse Code Multiple Access (SCMA) system, the accuracy of channel estimation has a significant impact on decoding performance. Various methods, so far, have been developed for channel estimation. Most of these methods regard channel estimation as a parameter estimation problem of linear models. However, these methods require lots of time-frequency resources to ensure high estimation accuracy. In massive connection scenarios, high pilot overhead makes the spectrum resource more scarce. Therefore, the drawback of conventional channel estimation methods limits the further improvement of system capacity in the Internet of Things(IoT) when time-frequency resources is restricted. To address this problem, in this paper, we propose an efficient channel estimation scheme and sparse pilot structure design method in SCMA system based on complex-valued sparse autoencoder which is effective to learn features of wireless channel. Complex-valued sparse autoencoder is a kind of neural network with complex-valued weights. It contains two parts: encoder and decoder. In our work, the encoder part is used to realize pilot design. Channel estimation is implemented by the decoder. Complex-valued weights obtained from training are used as baseband pilots. Compared with maximum likelihood channel estimation (MLE) of linear model, the proposed method can achieve higher channel estimation accuracy with more sparse pilot structure. The bit-error rates performance of the SCMA receiver in our work is very close to that of the perfect channel state information (CSI).

Design of Intelligent Reflecting Surface Phase Shifts for Uplink SCMA

In this letter, we consider an intelligent reflecting surface (IRS) assisted uplink sparse code multiple access (SCMA) system. We first derive an upper bound on word error rate (WER), based on which we design IRS phase shifts to minimize WER. Specifically, we propose two IRS design algorithms, semidefinite relaxation (SDR)-based approach and gradient ascent approach. We also provide computational complexity analysis. Then we derive an upper bound on average WER and analyse error performance. Finally, simulations demonstrate that both algorithms have performance similar to the known approach, while the proposed gradient ascent approach has the least computational complexity.

Cross-Layer Codebook Allocation for Uplink SCMA and PDNOMA-SCMA Video Transmission Systems and a Deep Learning-Based Approach

Sparse code multiple access (SCMA) and power-domain nonorthogonal multiple access (PDNOMA) have been considered for use with fifth-generation (5G) and beyond mobile communications, in order to increase the bandwidth efficiency, reduce the latency in terms of waiting for resources, and increase the numbers of connections. Existing codebook allocation schemes for SCMA and PDNOMA-SCMA systems consider only the physical layer. We propose an iterative cross-physical-and-application-layer codebook allocation for uplink video communications. We allocate the codebook according to the channel state information of the physical layer, and codebooks are exchanged among users to minimize the sum of the video mean square error in order to increase the average peak signal-to-noise ratio (PSNR). Furthermore, we propose a deep neural network (DNN) approach to reduce the computational complexity. We propose a novel postprocessing algorithm that is applied to the DNN output to ensure the constraints of codebook allocation are satisfied. Simulation results show that the proposed iterative PDNOMA-SCMA cross-layer codebook allocation scheme outperforms an existing scheme that uses only the physical layer by 1.27 dB in terms of PSNR (video quality) for 12 users, four subcarriers, and SNR = 21 dB. The proposed DNN-based approach has an execution time that is 67% lower than the proposed iterative PDNOMA-SCMA cross-layer codebook allocation scheme, at the cost of a performance degradation of 0.84 dB.

Resource Allocation in the RIS Assisted SCMA Cellular Network Coexisting With D2D Communications

The cellular network coexisting with device-to-device (D2D) communications has been studied extensively. Reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are promising technologies for the evolution of 5G, 6G and beyond. Besides, sparse code multiple access (SCMA) is considered suitable for next-generation wireless network in code-domain NOMA. In this paper, we consider the RIS-aided uplink SCMA cellular network simultaneously with D2D users. We formulate the optimization problem which aims to maximize the cellular sum-rate by jointly designing D2D users’ resource block (RB) association, the transmitted power for both cellular users and D2D users, and the phase shifts at the RIS. The power limitation and users’ communication requirements are considered. The problem is non-convex, and it is challenging to solve it directly. To handle this optimization problem, we propose an efficient iterative algorithm based on block coordinate descent (BCD) method. The original problem is decoupled into three subproblems to solve separately. Simulation results demonstrate that the proposed scheme can significantly improve the sum-rate performance over various schemes.

A Low-Complexity Detector for Uplink SCMA by Exploiting Dynamical Superior User Removal Algorithm

Sparse Code Multiple Access (SCMA) is capable of ameliorating the performance of wireless communication systems for massive connectivity. The sparsity of the codeword allows SCMA to employ the message-passing algorithm (MPA) at the receiver to address the connection overload. The detection complexity of traditional MPA, on the other hand, is exponentially exploding with the increasing number of connected devices. In this paper, a low-complexity MPA scheme is developed with the threshold and dynamical user removal algorithm (TB-RMPA) based on shuffled MPA (SMPA). By introducing the concept of Iterative Elimination of Dominated Strategies (IEDS) in game theory, a novel method of assigning different dominance levels to users is proposed hereby, and a threshold criterion is discussed to judge the reliability of user decoding. Inspired by this, users with reliable decoding in the iterative process will be dynamically eliminated in each iteration. The numerical analysis revealed that the new TB-RMPA detector’s implementation complexity decreases significantly compared with SMPA, while the symbol error ratio performance degrades unnoticeably.

Sparse Code Multiple Access Over Universal Filtered Multicarrier

In this paper, the uplink sparse code multiple access over universal filtered multicarrier (SCMA-UFMC) is studied. In SCMA-UFMC, each sub-band of UFMC is occupied by multiple users through SCMA. Note that the even-component of frequency domain (FD) received signal suffers from neither inter-sub-band interference nor inter-sub-carrier interference while the odd-component suffers from both. The message passing algorithm (MPA) is first employed to perform multiuser detection by utilizing only the even-component. Next, to improve the performance, the odd-component is further utilized. In this improvement, reliable estimates obtained by MPA over even-component are reconstructed and cancelled from odd-component, and then only a very small number of most-unreliable user data are refined by maximum likelihood method. Moreover, the union bound of the average symbol error probability (ASEP) of maximum likelihood detection using only even-component is derived, from which it is shown that ASEP is sub-band-independent. Finally, simulation results are given to demonstrate the performance of the proposed scheme and the effectiveness of the analytical ASEP result.

Deep Neural Network Aided Low-Complexity MPA Receivers for Uplink SCMA Systems

Sparse code multiple access (SCMA) has exhibited superiority in spectrum efficiency, which is particularly essential in the Internet of Things (IoT) system where a steadily increasing number of device connections are accommodated. However, the computational complexity of the conventional message passing algorithm (MPA) for the multiuser SCMA detection increases exponentially with the degree of resource nodes (RNs). To address this issue, two low complexity MPA schemes are proposed by utilizing the sparse feature of codewords. First, a sorted MPA (SMPA) detector is introduced to reduce the message exchanging from RNs to variable nodes (VNs) by dropping the redundant superposed constellation points outside a belief interval. Next, in order to further speed up the sorting process of the Euclidean distances between the received signal and codeword combinations, a deep neural network aided MPA (DNNMPA) is proposed, in which, the DNN behaves as a function approximator to generate the belief interval and operates in parallel with the initialization procedure before iterative message passing. Simulation results illustrate that the proposed SMPA and DNNMPA detectors significantly reduce the computational complexity of the conventional MPA one, but with comparable decoding capabilities, for the uplink SCMA system.

Intelligent Reflecting Surface-Assisted Uplink SCMA System

Intelligent reflecting surface (IRS) is a promising technique to enhance performance of wireless systems due to its ability to smartly reconfigure the signal propagation environment. Sparse code multiple access (SCMA) is code-domain non-orthogonal multiple access technique suitable for the next-generation wireless networks. In this letter, we consider IRS-assisted uplink SCMA (I-SCMA) for energy and spectrally-efficient wireless systems. Message passing algorithm (MPA)-based signal detection is proposed to detect multiple users’ symbols in uplink I-SCMA. The diversity orders of I-SCMA for random and coherent phase shifting are derived. The symbol error rate and the sum-rate performance of the uplink I-SCMA system are analyzed. Further, the impact of overloading factor, the number of reflecting surfaces, the number of phase shifts of reflected signals are studied. Simulation results demonstrate the benefits of I-SCMA in terms of the spectral and energy efficiency over conventional SCMA and other NOMA techniques.

IMPACT OF SUBCARRIER ALLOCATION BY ROWS ON TOTAL SUM RATE FOR SCMA SYSTEMS

Sparse code multiple access (SCMA) is a promising technique of non-orthogonal multiple access technology (NOMA). SCMA has the potential to increase the Total Sum Rate (TSR) in 5G systems. Previous works have considered the Fixed and Dynamic resource allocation based on columns (user) criterion. In this paper, we propose resource allocation based on rows (subcarriers). The numerical results show that our approach improves the TSR in an SCMA uplink channel.

Improving Spectral Efficiency in the SCMA Uplink Channel

The Third Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU) identified the technical requirements that the fifth generation of mobile communications networks (5G) had to meet; within these parameters are the following: an improved data rate and a greater number of users connected simultaneously. 5G uses non-orthogonal multiple access (NOMA) to increase the number of simultaneously connected users, and by encoding data it is possible to increase the spectral efficiency (SE). In this work, eight codewords are used to transmit three bits simultaneously using Sparse Code Multiple Access (SCMA), and through singular value decomposition (SVD) the Euclidean distance between constellation points is optimized. On the other hand, applications of machine intelligence and machine intelligence in 5G and beyond communication systems are still developing; in this sense, in this work we propose to use machine learning for detecting and decoding the SCMA codewords using neural networks. This paper focuses on the Use Case of enhanced mobile broadband (eMBB), where higher data rates are required, with a large number of users connected and low mobility. The simulation results show that it is possible to transmit three bits simultaneously with a low bit error rate (BER) using SVD-SCMA in the uplink channel. Our simulation results were compared against recent methods that use spatial modulation (SM) and antenna arrays in order to increase spectral efficiency. In adverse Signal-to-Noise Ratio (SNR), our proposal performs better than SM, and antenna arrays are not needed for transmission or reception.

Joint Rate and Fairness Improvement Based on Adaptive Weighted Graph Matrix for Uplink SCMA With Randomly Distributed Users

Developing resource allocation algorithms for the uplink sparse code multiple access (SCMA) scheme to satisfy multiple objectives is challenging, especially where users are randomly distributed. In this paper, we aim to address this challenge by developing a joint resource allocation method as a multi-objective optimization (MO) problem to maximize the average sum rate and fairness among users as key and sub-key objectives, respectively. For this purpose, the exact analytical expressions for the average sum rate and users' individual rate are extracted based on an adaptive weighted graph matrix (AWGM). An AWGM matrix beneficially replaces the factor graph and the power allocation matrices to simplify the MO problem based on the asymmetric modified bipartite matching (AMBM) algorithm. The power allocation strategy is utilized during the optimal resource assignment process using the AMBM algorithm. After the AMBM process, we propose a low-complexity four-step algorithm to obtain the AWGM. The simulation results show that our proposed method can compromise and improve the multiple objectives' performance and guarantees a stable range of network performance at different times.

Reconfigurable Intelligent Surface-Assisted Uplink Sparse Code Multiple Access

Reconfigurable intelligent surface-empowered communication (RIS) and sparse code multiple access (SCMA) are promising candidates for future generations of wireless networks. The former enhances the transmission environments, whereas the latter provides a high spectral efficiency transmission. This letter proposes, for the first time, a low-cost design for RIS-assisted uplink SCMA (SCMA-RIS) scheme to improve the conventional SCMA spectrum efficiency. The message passing algorithm (MPA) is utilized and modified to decode the SCMA-RIS transmitted signals. Moreover, a low-complexity decoder for the SCMA-RIS scheme is proposed to significantly reduce the MPA decoding complexity and improve the bit error rate performance of the conventional SCMA. Monte Carlo simulations and complexity analysis are presented, which support the findings.

Uplink SCMA Codebook Reuse Transmission and Reception Scheme

Sparse code multiple access (SCMA) is a new non-orthogonal multiple access scheme suitable for 5G communication, which can effectively improve spectrum efficiency and support massive connections. Multiple users in the SCMA system realize the sharing of the same time-frequency resources by mapping data into codewords of a special code book (Code Book, CB). A typical SCMA system increases the spectrum utilization to 150%. In order to further improve the system spectrum utilization and increase the number of user connections, this paper proposes an uplink SCMA codebook reuse transmission and reception scheme (CB-Reuse-SCMA), which reuse a codebook to multiple users. The base station allocates <i>β</i> users to each codebook at the same time, so that <i>β</i> users use the same codebook to transmit uplink information. On the receiver of the base station, an improved Logarithmic Message Passing Algorithm (Log-MPA) is designed for multi-user detection, so that the base station can correctly detect the data of <i>β</i> users who reuse the same codebook. Simulation results show that at the time <i>β</i> = 2, the proposed scheme increased the spectrum utilization to 300%; at the time <i>β</i> = 3, the proposed scheme increased the spectrum utilization to 450%. Compared with the traditional SCMA scheme where the user has an exclusive codebook, the proposed scheme improves the spectrum utilization to <i>β</i> × 150% through codebook reuse at the expense of a small amount of SNR gain, which improves the throughput and user connection by <i>β</i> times number.

Low Complexity Decoding Algorithm for Uplink SCMA Based on Aerial Spherical Decoding

As a new non-orthogonal multiple access technology for 5G massive machine type communication scenario, the sparse code multiple access (SCMA) has greatly improved the spectrum efficiency due to the high connection density. SCMA combines QAM (Quadrature Amplitude Modulation) modulation and sparse spreading into a codebook set to obtain forming gain. The user binary bit data is directly mapped into multi-dimensional codewords in the transmitter. The receiver uses the message passing algorithm (MPA) for multi-user detection to achieve efficient decoding. However, MPA is a good solution for SCMA, though its high complexity limits the application in practical systems. In order to reduce the complexity of MPA, this paper proposed a low-complexity decoding algorithm based on the serial spherical decoding message passing algorithm (SSD-MPA), which greatly enhanced the practicability of the SCMA system. The SSD-MPA took into account the distribution characteristics of the Gaussian noise and the reliability of the constellation points, and only updates the trusted constellation point messages of nodes within the spherical radius. At the same time, it used a serial strategy to speed up the convergence rate. The simulation results have shown that when the radius sets properly, the algorithm reduces the complexity by about 2/3 compared with the original MPA, and the performance was almost lossless.

A Partial Gaussian Tree Approximation (PGTA) Detector for Random Multiple Access Oriented SCMA Uplink With Codebook Collisions

By means of random multiple access (RMA), there is a prevailing tendency for massive machine-type terminals to have access into networks without numerous signaling interactions and frequent resource scheduling. Being one of the promising techniques to fulfill this requirement, sparse code multiple access (SCMA) is anticipated to notably broaden connections for the communication system. However, when applying RMA in the SCMA uplink, as massive terminals would randomly transmit their packets in an uncoordinated way, it is quite possible for some terminals to share an identical codebook so as to introduce the codebook collision problem. In this case, an indefinite number of terminals with possible codebook collisions would competitively share a same block of channel resources, resulting in an overloading status and deteriorated performance. To deal with this problem, we illustrate first the potential utilization of codebook collisions for RMA and propose then a partial Gaussian tree approximation (PGTA) detector to relieve heavy computational burden due to overloading conditions. With approximate decomposition of likelihood functions that has rarely been discussed, the proposed PGTA detector with proper message passing rules is able to approach the performance of a traditional message passing detector with considerably lower complexity.

On the Complexity Reduction of Uplink Sparse Code Multiple Access for Spatial Modulation

Multi-user spatial modulation (SM) assisted by sparse code multiple access (SCMA) has been recently proposed to provide uplink high spectral efficiency transmission. The message passing algorithm (MPA) is employed to detect the transmitted signals, which suffers from high complexity. This paper proposes three low-complexity algorithms for the first time to the SM-SCMA. The first algorithm is referred to as successive user detection (SUD), while the second algorithm is the modified version of SUD, namely modified SUD (MSUD). Then, for the first time, the tree-search of the SM-SCMA is constructed. Based on that tree-search, another variant of the sphere decoder (SD) is proposed for the SM-SCMA, referred to as fixed-complexity SD (FCSD). SUD provides a benchmark for decoding complexity at the expense of bit-error-rate (BER) performance. Further, MSUD slightly increases the complexity of SUD with a significant improvement in BER performance. Finally, FCSD provides a near-optimum BER with a considerable reduction of the complexity compared to the MPA decoder and also supports parallel hardware implementation. The proposed algorithms provide flexible design choices for practical implementation based on system design demands. The complexity analysis and Monte-Carlo simulations of the BER are provided for the proposed algorithms.

Joint uplink SCMA codebook design for correlated fading channels

Sparse Code Multiple Access (SCMA) is currently one of the most intensively investigated Multicarrier Non-Orthogonal Multiple Access (NOMA) technology for 5G wireless communication system. Careful design of SCMA codebook is one of the major performance criteria of uplink SCMA systems. We took the challenge of tackling SCMA Codebook design problem in correlated Rayleigh fading channels. We propose a novel joint codebook design approach followed by a new signal model, where the design problem is posed as a Quadratically Constrained Quadratic Program (QCQP) which is dealt using Matrix-lifting Semidefinite Relaxation (MSDR). Extensive numerical simulations are presented to provide the validity of proposed codebook comparing with some other well known designs.

Low-Overhead Evaluation of Multiuser Detection Performance for Physical-Layer Multiple Access Systems

In order to facilitate massive connectivity in fifth-generation (5G) systems and make full use of advanced coding schemes, designing effective multiuser detection algorithms is necessary for mitigating the interference in multiple access systems. To evaluate the performance of different detection algorithms, the conventional method is implementing the Monte Carlo (MC) simulation to estimate the detection error rate. In this paper, we present a novel simulation scheme based on adaptive importance-sampling (AIS) theory, which accelerates the simulation speed for estimating the extremely low detection error rate in multiple access systems. Specifically, by restricting the generation of random codewords to the joint Gaussian distribution biased with scaling parameters, two algorithms are proposed to determine the optimal biased parameters such that the estimated variance or the cross-entropy resulted in the AIS simulation is minimized respectively. Our proposed simulation scheme is compared with the standard MC simulation in the performance evaluation of message passing algorithm (MPA) for uplink sparse code multiple access (SCMA) system. Numerical results show that our proposed scheme provides a feasible estimation of extremely low detection error rate and achieves significant performance gain with reduced simulation overhead.

An Efficient Codebook Design for Uplink SCMA

Sparse code multiple access (SCMA) is one of the most intensively investigated non-orthogonal multiple access (NOMA) techniques for the fifth generation communication (5G) system. In this paper, a new multi-stage optimization approach for codebook design is proposed for the uplink SCMA system. The proposed schemes includes two parts: i) multi-dimensional mother constellation design ii) mapping matrix design. Firstly, the multi-dimensional complex mother constellation design is implemented by a series of two-dimensional real constellations which can be obtained by the lattice theory and the symbol switch algorithm (SSA). The Secondly, we construct user-specific mapping matrix by optimizing the phase rotation of the user constellation to effectively reduce the interference between the multi-user codewords superimposed on every single time-domain resource elements (REs). Finally, the multi-user codebooks are generated by combining the multi-dimensional mother constellation and the user-specific mapping matrix. The simulation results demonstrate that the proposed codebooks can greatly decrease bit error rate (BER) value compared to the existing codebooks under different channels, different codebook sizes and different overloading ratios, and the performance is also improved for the large-scale SCMA codebook. More importantly, in order to verify the proposed codebook applicability, two different message passing algorithm (MPA) are used to simulate the SCMA system with different codebook sizes, both of which obtain faster convergence speed and better BER performance than other existing codebooks.