Low-Complexity Near-ML Decoding of Large Non-Orthogonal STBCs using Reactive Tabu Search

TL;DR

This paper introduces a reactive tabu search algorithm for decoding large non-orthogonal space-time block codes, achieving near-optimal performance with reduced complexity compared to previous methods.

Contribution

The paper presents a novel RTS-based decoding algorithm for large non-orthogonal STBCs from cyclic division algebras, demonstrating near-ML performance with lower complexity.

Findings

Achieves 10^{-3} BER at 0.5 dB SNR for 12x12 STBC with 288 real dimensions.

Close to capacity performance with rate-3/4 turbo code at 18 bps/Hz.

Effective even with correlated channels and iterative decoding schemes.

Abstract

Non-orthogonal space-time block codes (STBC) with {\em 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 large dimensions has been a challenge. In this paper, we present a reactive tabu search (RTS) based algorithm for decoding non-orthogonal STBCs from cyclic division algebras (CDA) having large dimensions. Under i.i.d fading and perfect channel state information at the receiver (CSIR), our simulation results show that RTS based decoding of $12\times 12$ STBC from CDA and 4-QAM with 288 real dimensions achieves $i)$ $10^{-3}$ uncoded BER at an SNR of just 0.5 dB away from SISO AWGN performance, and $ii)$ a coded BER performance close to within about 5 dB of the theoretical MIMO capacity, using rate-3/4 turbo code at a spectral efficiency of 18 bps/Hz. RTS is shown to achieve near SISO AWGN performance with less number of dimensions than with LAS algorithm (which we reported recently) at some extra complexity than LAS. We also report good BER performance of RTS when i.i.d fading and perfect CSIR assumptions are relaxed by considering a spatially correlated MIMO channel model, and by using a training based iterative RTS decoding/channel estimation scheme.