Downlink Sparse Code Multiple Access Research Articles

A Novel Image-Classification-Based Decoding Strategy for Downlink Sparse Code Multiple Access Systems.

The introduction of sparse code multiple access (SCMA) is driven by the high expectations for future cellular systems. In traditional SCMA receivers, the message passing algorithm (MPA) is commonly employed for received-signal decoding. However, the high computational complexity of the MPA falls short in meeting the low latency requirements of modern communications. Deep learning (DL) has been proven to be applicable in the field of signal detection with low computational complexity and low bit error rate (BER). To enhance the decoding performance of SCMA systems, we present a novel approach that replaces the complex operation of separating codewords of individual sub-users from overlapping codewords using classifying images and is suitable for efficient handling by lightweight graph neural networks. The eigenvalues of training images contain crucial information, such as the amplitude and phase of received signals, as well as channel characteristics. Simulation results show that our proposed scheme has better BER performance and lower computational complexity than other previous SCMA decoding strategies.

Sum-Rate Maximization of IRS-Aided SCMA System

We study an intelligent reflecting surface (IRS)-aided downlink sparse code multiple access (SCMA) system for massive connectivity in future machine -type communication networks. Our objective is to maximize the system sum-rate subject to the constraint of minimum user data rate, the total power of base station, SCMA codebook structure, and IRS channel coefficients. To this end, a joint optimization problem involving IRS phase vector, factor graph matrix assignment, and power allocation problem is formulated, which is non-convex in nature. This problem is solved by developing an alternating optimization (AO) algorithm. A key idea is to first divide the formulated non-convex problem into three subproblems (i.e., factor graph matrix assignment, power allocation, and phase vector of IRS) and then tackle them iteratively. The validity of the proposed schemes is shown using the simulation results. Moreover, compared to the SCMA system without IRS, a significant performance improvement in the IRS-aided SCMA system is shown in terms of achievable sum-rate.

Intelligent Reflecting Surface-Aided Downlink SCMA

Recently, intelligent reflecting surfaces (IRSs) and nonorthogonal multiple-access (NOMA) have emerged as energy and spectrally efficient techniques for next-generation wireless networks. Thus, in this article, IRS-aided downlink sparse code multiple access (SCMA) system is analyzed for the next-generation wireless networks. In the proposed IRS-aided SCMA (IRS-SCMA), the base station (BS) communicates with single-antenna users with the help of an IRS. The impact of different phase shift methods at the IRS panel is studied in the IRS-SCMA, and observed that the average phase shift method has a very close performance to the perfect phase shift (optimal) with low complexity. We analyze symbol error rate and sum-rate performance of the IRS-SCMA. Simulation results show that the proposed IRS-SCMA outperforms the corresponding IRS-aided power domain nonorthogonal multiple access (PD-NOMA), and SCMA without IRS schemes. Further, the impacts of various system's parameters, such as the number of IRS reflecting elements, channel fading, SCMA codebooks, IRS location, and the number of users on the system performance are studied.

A Knowledge-Based Deep Learning Detection Scheme for Downlink SCMA Systems

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access technology for future wireless communication systems. In the SCMA detector, deep learning (DL) technology has been adopted to improve the detection efficiency. However, most previous schemes are completely data-driven designed without using prior knowledge. In this letter, we propose a knowledge-based deep learning detection scheme (K-DLD) that incorporates prior knowledge into SCMA detection to lighten the neural network. Moreover, we propose to use a thinner but deeper network to further reduce the model complexity. Simulation results show that the proposed schemes significantly reduce the computation time without any performance loss.

A Novel Multitask Learning Empowered Codebook Design for Downlink SCMA Networks

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA, the design of good sparse codebooks and efficient multiuser decoding have attracted tremendous research attention in the past few years. This letter aims to leverage deep learning to jointly design the downlink SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. Compared to conventional SCMA schemes, our numerical results show that the proposed E2E-SCMA leads to significant improvements in terms of error rate and computational complexity.

Downlink SCMA Codebook Design With Low Error Rate by Maximizing Minimum Euclidean Distance of Superimposed Codewords

Sparse code multiple access (SCMA), as a codebook-based non-orthogonal multiple access (NOMA) technique, has received research attention in recent years. The codebook design problem for SCMA has also been studied to some extent since codebook choices are highly related to the system's error rate performance. In this paper, we approach the SCMA codebook design problem by formulating an optimization problem to maximize the minimum Euclidean distance (MED) of superimposed codewords under power constraints. While SCMA codebooks with a larger MED are expected to obtain a better BER performance, no optimal SCMA codebook in terms of MED maximization, to the authors' best knowledge, has been reported in the SCMA literature yet. In this paper, a new iterative algorithm based on alternating maximization with exact penalty is proposed for the MED maximization problem. The proposed algorithm, when supplied with appropriate initial points and parameters, achieves a set of codebooks of all users whose MED is larger than any previously reported results. A Lagrange dual problem is derived which provides an upper bound of MED of any set of codebooks. Even though there is still a nonzero gap between the achieved MED and the upper bound given by the dual problem, simulation results demonstrate clear advantages in error rate performances of the proposed set of codebooks over all existing ones not only in AWGN channels but also in some downlink scenarios that fit in 5G/NR applications, making it a good codebook candidate thereof. The proposed set of SCMA codebooks, however, are not shown to outperform existing ones in uplink channels or in the case where non-consecutive OFDMA subcarriers are used. The correctness and accuracy of error curves in the simulation results are further confirmed by the coincidences with the theoretical upper bounds of error rates derived for any given set of codebooks.

An optimized code book design and assignment based on 32-QAM constellation in downlink SCMA systems

Sparse Code Multiple Access (SCMA) is a multi-dimensional codebook based on a class of Non-Orthogonal Multiple Access (NOMA) to provide many users through non-orthogonal resource elements without detection complexity in 5G wireless communications. The codebook design is one of the main criteria in SCMA downlink systems. This paper proposes an efficient SCMA joint codebook design and assignment model to reduce the detection complexity and maximize the Minimum Euclidean Distance (MED) in Rayleigh fading channels. Initially, we express codebook assignment, which is allotted to group the highest priority subcarriers and then assign to the specific user using k-medoid clustering algorithm and codebook design (i.e., sparse mapping matrix and factor graph constellation design). The obtained mapping matrix with a factor graph uses four ring 32-Quadrature Amplitude Modulation (QAM) features of SCMA that carefully design the mother constellation points for each codebook. In particular, the 32-QAM mother constellation points are obtained for each codebook by phase rotation. In this manner, the codebook is designed without any detection complexity. Further, the Message Passing Algorithm (MPA) is used to reduce the multi-user interference in the receiver side. The experimental results depicts that the proposed SCMA codebook approach achieves low Bit Error Rate (BER) and High Spectral Efficiency (SE) performance comparing with existing known designs in Rayleigh channels.

Design of SCMA Codebooks Based on Golden Angle Modulation

In this paper, we propose to use golden angle modulation (GAM) points to construct codebooks for uplink and downlink sparse code multiple access (SCMA) systems. We provide two categories of codebooks with one and two optimization parameters, respectively. The advantages of the proposed design method are twofold: 1) the number of optimization variables is independent of codebook and system parameters; and 2) it is simple to implement. In the downlink, we use GAM points to build a multidimensional mother constellation for SCMA codebooks, while in the uplink GAM points are directly mapped to user codebooks. The proposed codebooks exhibit good performance with low peak to average power ratio compared to the codebooks proposed in the literature based on constellation rotation and interleaving.

Secure Transmission With Randomized Constellation Rotation for Downlink Sparse Code Multiple Access System

Sparse code multiple access (SCMA) is a promising candidate air interface of next-generation mobile networks. In this paper, we focus on a downlink SCMA system where a transmitter sends confidential messages to multiple users in the presence of external eavesdroppers. Consequently, we develop a novel secure transmission approach over physical layer based on a highly structured SCMA codebook design. In our proposed scheme, we rotate the base constellations (BCs) with random angles by extracting channel phases from the channel state information. By employing randomized constellation rotation, the security of downlink SCMA can be ensured. In addition, a tight SCMA upper bound is introduced to guide the design of the encrypted codebook. As a result, we propose an approach to avoid the significant error rate performance loss caused by using codebooks that are designed using our method. The proposed upper-bound-aided codebook design scheme can select relatively good codebooks with low complexity. By combining SCMA codebook design and secure communication, our scheme ensures security for massive quantities of users with low encrypted and decrypted complexity at the cost of transmission rate and possible error rate performance loss. Moreover, the proposed scheme can achieve robustness against channel estimation errors. Analyses and Monte Carlo simulations confirm the effectiveness of our scheme.

A low complexity detector for downlink SCMA systems

Sparse code multiple access (SCMA) is a novel non-orthogonal multiple access scheme for 5G systems, in which the logarithm domain message passing algorithm (Log-MPA) is applied at the receiver to achieve near-optimum performance. However, the computational complexity of Log-MPA detector is still a big challenge for practical implementation, especially for energy-sensitive user equipments in the downlink scenario. A region-restricted detector with an improved Log-MPA (RRL detector) is proposed for downlink SCMA systems, in which the complexity is reduced from two perspectives. To avoid unnecessary calculations when searching the superposition constellation exhaustively, the proposed RRL detector updates the function nodes only within a restricted search region. While constellation points outside the search region are neglected, the performance is well maintained which is verified by simulations. Besides, the original Log-MPA heavily relies on exponential operations, resulting in high computational complexity. To solve this problem, an improved Log-MPA is also put forward in this study to make a better compromise between complexity and performance. Simulation results show that the complexity of the RRL detector is reduced considerably while the bit error rate performance degrades unnoticeably.