Code-Aided Direction Finding in Turbo-Coded Square-QAM Transmissions

Abstract

We investigate the problem of direction of arrival (DOA) estimation from turbo-coded square-QAM- modulated signals. We propose a new code-aided (CA) maximum likelihood (ML) direction finding technique that exploits the soft information obtained from the soft-input soft-output (SISO) decoder in the form of log-likelihood ratios (LLRs). Unlike standard estimation techniques, the proposed method improves the system performance by appropriately embedding the direction finding and receive beamforming tasks within the turbo iteration loop. In fact, the DOA estimates and the soft information are iteratively exchanged between the decoding and estimation blocks, respectively, according to the so called-turbo principle. Simulation results show that the new CA DOA estimation scheme lies between the two extreme direction finding schemes: completely non-data aided (NDA) and data-aided (DA) estimations. Moreover, the new CA DOA estimator reaches the corresponding CA Cramér-Rao lower bounds (CRLBs), over a wide range of practical SNRs thereby confirming its statistical efficiency in practice. The proposed scheme can be applied to systems, as well, when they are decoded with the turbo principle.