Abstract
Sparse code multiple access (SCMA) has emerged as a promising non-orthogonal multiple access technique for the next-generation wireless communication systems. Since the signal of multiple users is mapped to the same resources in SCMA, its detection imposes a higher complexity than that of the orthogonal schemes, where each resource slot is dedicated to a single user. In this paper, we propose a low-complexity receiver for SCMA systems based on the radical variational free energy framework. By exploiting the pairwise structure of the likelihood function, the Bethe approximation is utilized for estimating the data symbols. The complexity of the proposed algorithm only increases linearly with the number of users, which is much lower than that of the maximum a posteriori detector associated with exponentially increased complexity. Furthermore, the convergence of the proposed algorithm is analyzed, and its convergence conditions are derived. Simulation results demonstrate that the proposed receiver is capable of approaching the error probability performance of the conventional message-passing-based receiver.
Highlights
The generation wireless communication systems aim for an increased spectral efficiency and low latency [1], [2]
In the sparse code multiple access (SCMA) system, the number of users is larger than the number of resource blocks, which leads to a rankdeficient system
The damping factor of the proposed algorithm is α = 0.3.1 It can be seen from both figures that the proposed algorithm matches the performance of the message passing algorithm (MPA) receiver, but the complexity of the conventional MPA receiver increases exponentially with the number of users
Summary
The generation wireless communication systems aim for an increased spectral efficiency and low latency [1], [2] In this context, sparse code multiple access (SCMA) has recently attracted substantial attention, since it is capable of supporting large-scale connectivity and improved coverage [3]. The rank-deficient SCMA system results in a factor graph having short cycles, for which the message passing algorithm may not be able to converge. We propose a low-complexity receiver based on Bayesian inference [10]. We develop a low-complexity iterative receiver for our SCMA system. Our simulation results show that the proposed algorithm approaches the optimal MAP detector’s performance despite significantly reducing its complexity. C denotes a constant number; | · | denotes the modulus of a complex number or the cardinality of a set; · denotes a 2 matrix norm; ∝ represents equality up to a constant normalization factor; (i, j) denotes the corresponding variables are included in a pairwise potential
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