Subspace algorithms for error localization with DFT codes

We propose two subspace algorithms for error localization with quantized DFT codes. The algorithms are similar to the MUSIC and the minimum-norm algorithms employed in array signal processing for direction of arrival (DOA) estimation. We present the algorithms in a generalized form with a variable dimension syndrome covariance matrix. Simulation results show that their localization performances are similar, and they achieve the peak values when the rank of the covariance matrix reaches the maximum value. They also perform better than the coding theoretic approach over a broad range of channel-error-to-quantization-noise ratio.