| dc.description.abstract | Sparse code multiple access (SCMA) uses multi-dimensional sparse codewords to transmit user data and increases utilization of resources. Conventional decoder adopts massage passing algorithm (MPA) to recover user data based on the sparse property, and achieves good performance. However, the complexity grows exponen-tially as the codebook size increases. Expectation propagation algorithm (EPA), de-rived from machine learning (ML), has been proposed for SCMA decoding and has turned the complexity from exponential growth to linear growth. Thus, it is much suitable for implementation. In this paper, we propose convergence-aware EPA, which incorporates three termination schemes with user defined thresholds respec-tively so that the decoder can stop unnecessary calculations to reduce complexity. The user termination scheme must be combined with the iteration constraint to avoid misjudgement. The antenna termination scheme can stop the computations related with certain antennas having strong channel gains. Only possible codewords are con-sidered in the codebook reduction scheme to eliminate unnecessary calculations for posterior probability. From simulation results, we show that the proposed method can strike a balance between complexity and performance with different threshold set-tings. Furthermore, the hardware of the EPA decoder is implemented supporting 4 receive antennas and, 4 iterations given a 16-point codebook. The gated clock design is applied to realize the early termination. Hardware sharing method helps to reduce the complexity of RN computation units, antenna probability computation units and posterior probability computation units for about 67%, 75%, and 75% with the same performance. The synthesis result shows that maximum operation frequency and throughput of our work are 156.25MHz and 193.97Mbps, respectively. With the ter-mination schemes, the power consumption is reduced from 460.6mW to 254.1mW at 0.9V supply voltage and 156.25MHz operating frequency. | en_US |