Abstract
Sparse code multiple access (SCMA) is one of the promising schemes to meet high connectivity and spectral efficiency in the future wireless networks. The iterative detectors, for example message passing algorithm (MPA), can provide near optimal multiuser detection (MUD) performance but becomes infeasible when the codebook size is large or the overloading factor is high. Recently, sphere decoding (SD) has been considered in the MUD of SCMA by rewriting the generalized transmission into a linear system. In this work, we first review the state-of-the-art SD-based detectors for SCMA: sphere decoding for SCMA (SD-SCMA) and generalized SD-SCMA (GSD-SCMA). We not only explain the state-of-the-art in a comprehensive way, but also exploit the sorted QR decomposition and Schnorr-Euchner enumeration to accelerate the tree search. Although GSD-SCMA overcomes the codebook constraint of SD-SCMA, its computational complexity is extremely sensitive to the overloading factor. To satisfy the trade-off between complexity and MUD performance, we propose two pruning algorithms, PRUN1 and PRUN2, and introduce the simplified GSD-SCMA (SGSD-SCMA). In the paper, error probabilities of the proposed pruning algorithms are derived. Simulation results show that the proposed detector outperforms the iterative detectors and SD-based state-of-the-art when the overloading factor is moderate and the codebook size is large.
Highlights
W ITH the development of Internet of Things (IoT), people and ubiquitous devices are going to be connected in the wireless networks
STATE-OF-THE-ART sphere decoding (SD)-BASED Sparse code multiple access (SCMA) DETECTORS we provide a comprehensive description of SD-based SCMA detector state-of-the-art, which are SDSCMA [22] and GSD-SCMA [23]
As the complexity of SD-based detectors is relevant to the number of visited nodes in the tree search which is unpredictable, we provide the floating point operations (FLOPs) expression of different constituting components of the detectors
Summary
W ITH the development of Internet of Things (IoT), people and ubiquitous devices are going to be connected in the wireless networks. At the receiver of SCMA, the maximum likelihood (ML) algorithm can provide optimal performance for MUD by joint decoding, at the cost of exhaustively testing all combinations of the transmitted codewords. It becomes infeasible when the number of users increases or the codebook size gets large. The proposed detectors in [20], [21] show better performance and lower complexity than the MPA, they are not applicable to generalized SCMA multidimensional codebooks. As GSD-SCMA performs a brute-force search on partial transmitted symbols, its computational complexity may surge when the codebook size or overloading factor is large. The important notations used in this paper are listed in the Table 1
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