Abstract

Turbo equalization schemes based on minimum mean square error criteria available in the literature for multiple-input multiple-output (MIMO) systems are computationally expensive, as they require a relatively large matrix inversion. In this article, we propose a suboptimal, successive interference cancelation (SIC)-based maximum a posteriori (MAP) decoding in doubly dispersive channels for orthogonal frequency division multiplexing (OFDM) MIMO systems (SIC-MAP-MIMO). SIC-MAP-MIMO leverages on the soft feedback symbol estimate to remove the intercarrier interference and coantenna interference from the received data thus making the subsequent MAP decoding simple. Extrinsic information transfer chart analysis supplemented with numerical simulation results show that SIC-MAP-MIMO achieves comparable BER performance to similar equalization schemes but with significant computational savings.

Highlights

  • Wireless communication based on multiple-input multiple-output (MIMO) systems has gained popularity due to the potential capacity increases it can provide [1]

  • Turbo-like iterative schemes are Namboodiri et al EURASIP Journal on Wireless Communications and Networking 2012, 2012:311 http://jwcn.eurasipjournals.com/content/2012/1/311 found to have superior performance compared to others, but they usually suffer from high computation complexity, albeit at varying degrees, and require high silicon area for implementation and high battery power for operation

  • Formulation of the proposed maximum a posteriori (MAP) receiver we present a low-complexity iterative receiver that implements successive interference cancelation (SIC), followed by MAP decoding for MIMO systems

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Summary

Introduction

Wireless communication based on MIMO systems has gained popularity due to the potential capacity increases it can provide [1]. Turbo Equalization (TE) proposed in [18] performs MMSE-based turbo estimation of the transmitted symbol on singlecarrier systems under static channel conditions, followed by LLR computation and BCJR decoding.

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