Journal
IEEE TRANSACTIONS ON COMMUNICATIONS
Volume 66, Issue 3, Pages 905-917Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2017.2776937
Keywords
Sparse regression codes; approximate message passing; low-complexity decoding; finite length performance; coded modulation
Funding
- EPSRC [EP/N013999/1]
- EPSRC Doctoral Training Award
- EPSRC [EP/N013999/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [1355086, EP/N013999/1] Funding Source: researchfish
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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 precomputation. 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.
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