Low Complexity Expectation Propagation Detection for SCMA Using Approximate Computing

Sparse code multiple access (SCMA) is one of the promising non-orthogonal multiple access (NOMA) techniques for the future wireless communication systems, which can provide more connections and higher spectral efficiency than orthogonal multiple access (OMA). In this paper, we propose a novel expectation propagation algorithm based on approximate computing to achieve low complexity and high performance SCMA detection, which is referred as approximate expectation propagation algorithm (AEPA). Three approximate approaches are provided for variable node update, function node update and log likelihood ratio calculation to reduce the algorithm complexity. The parameter optimization methods are also provided to get the trade-off between the detection performance and the algorithm complexity. Simulation and evaluation results show that AEPA can obtain more than 32% complexity reduction with only 0.1dB performance loss when the codebook size is 4 compared with the traditional expectation propagation algorithm (EPA), and the complexity reduction gain will become more significant when the codebook size is larger.