Low-Complexity Near-ML Decoding of Large Non-Orthogonal STBCs Using PDA

TL;DR

This paper introduces a probabilistic data association (PDA) decoding algorithm for large non-orthogonal space-time block codes from cyclic division algebras, achieving near-capacity performance with reduced complexity.

Contribution

The paper presents a novel PDA-based decoding method for large non-orthogonal STBCs, enabling efficient decoding with near-capacity performance and robustness to spatial correlation.

Findings

PDA algorithm achieves near SISO AWGN BER performance.

Performance within 5 dB of theoretical capacity.

Effective in channels with large coherence times.

Abstract

Non-orthogonal space-time block codes (STBC) from cyclic division algebras (CDA) having large dimensions are attractive because they can simultaneously achieve both high spectral efficiencies (same spectral efficiency as in V-BLAST for a given number of transmit antennas) {\em as well as} full transmit diversity. Decoding of non-orthogonal STBCs with hundreds of dimensions has been a challenge. In this paper, we present a probabilistic data association (PDA) based algorithm for decoding non-orthogonal STBCs with large dimensions. Our simulation results show that the proposed PDA-based algorithm achieves near SISO AWGN uncoded BER as well as near-capacity coded BER (within about 5 dB of the theoretical capacity) for large non-orthogonal STBCs from CDA. We study the effect of spatial correlation on the BER, and show that the performance loss due to spatial correlation can be alleviated by providing more receive spatial dimensions. We report good BER performance when a training-based iterative decoding/channel estimation is used (instead of assuming perfect channel knowledge) in channels with large coherence times. A comparison of the performances of the PDA algorithm and the likelihood ascent search (LAS) algorithm (reported in our recent work) is also presented.