A review of codebook design methods for sparse code multiple access

Abstract

<span lang="EN-GB">The progressions in telecommunication beyond the 5<sup>th</sup> generation have created a need to improve research drifts. The current 5G study has an important focus on non-orthogonal multiple access (NOMA) technology. sparse code multiple access (SCMA) is a promising technique within NOMA, enhancing the multi-user handling capability of next-generation communication. In the SCMA sphere, codebook designing and optimisation are essential research matters. This study conversed with different codebook design practises existing in the literature, analysing them for numerous parameters, including bit error rate (BER), an optimisation technique, and channel settings. From the analysis, the paper presents the efficiency of different approaches. The article also discusses the prospects and challenges of SCMA optimisation in practical implementation in various domains.</span>

CG-SCMA Codebook Design Based on Maximized Euclidian Distance

“…In [9], Hussain et al examined the effectiveness of various strategies of codebook generation and difficulties of SCMA optimization in real-world applications, across various applications. Zhang et al [10] reviewed selected codebook designs and available SCMA detectors after presenting a comprehensive survey on the state-of-the-art in SCMA and of its fundamental principles.…”

“…Users that need to their encoded data should construct codewords by multiplying their codebooks with encoded data. Finally, all users' codewords are multiplexed and sent to the destination [9], [14]. To guarantee good performance of the SCMA design, the potentially highest minimum Euclidian distance needs to be obtained.…”

“…The message passing algorithm (MPA) is an iterative parallel decoding method that works by moving extrinsic data from function nodes (FNs) to variables nodes (VNs) and vice versa [9], as shown in Fig. 1.…”

Design Low Complexity SCMA Codebook Using Arnold’s Cat Map

“…It achieves 10 −4 BER at 15 dB SNR for a fading channel with reduced PAPR, while for uplink resource allocation it achieves 10 −4 BER at 21 dB. In paper [8], Bonilla uses a similar approach as [6], [7], but in this case it relies on a minimum Euclidean distance (MED)-based detector at the receiver, with interleaving and phase rotation. It achieves 10 −4 BER for codebooks with 4 arrays, 8 arrays, and 16 arrays with E b /N o 5 dB, 6 dB, and 6.5 dB, respectively.…”