Techniques for improving the finite length performance of sparse superposition codes

Abstract

Sparse superposition codes are a recent class of codes introduced by Barron and Joseph for efficient communication over the AWGN channel. With an appropriate power allocation, these codes have been shown to be asymptotically capacity-achieving with computationally feasible decoding. However, a direct implementation of the capacity-achieving construction does not give good finite length error performance. In this paper, we consider sparse superposition codes with approximate message passing (AMP) decoding, and describe a variety of techniques to improve their finite length performance. These include an iterative algorithm for SPARC power allocation, guidelines for choosing codebook parameters, and estimating a critical decoding parameter online instead of pre-computation. We also show how partial outer codes can be used in conjunction with AMP decoding to obtain a steep waterfall in the error performance curves. We compare the error performance of AMP-decoded sparse superposition codes with coded modulation using LDPC codes from the WiMAX standard.

13 pages, 16 figures. To appear in IEEE Transactions on Communications

References in corpus (1)

• Replica Analysis and Approximate Message Passing Decoder for Superposition Codes

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• The Error Probability of Sparse Superposition Codes with Approximate Message Passing Decoding
• Using List Decoding to Improve the Finite-Length Performance of Sparse Regression Codes
• Orthogonal Sparse Superposition Codes for Ultra-Reliable Low-Latency Communications