Noise-Resilient Blind Deconvolution using Error-Correcting Codes

Future communication systems pose stringent requirements on communication latency between two nodes. One way to reduce the latency is by reducing control plane overhead by estimating the channel without training symbols. The combined process of estimating the channel and equalizing is called blind deconvolution. In this paper, our contribution is two-fold. First, we concatenate random coding (blind deconvolution using convex programming) solver with error-correcting code (ECC) decoder. Second, we propose an intelligent perturbation algorithm (IPA) that uses the ECC decoder to assist the random coding solver iteratively in solving the blind deconvolution problem. We also define the theoretical bounds for the optimal rate of the generator matrix of ECC. We have performed error-rate simulations for both Gaussian as well as Rayleigh channels. IPA provides 5 dB gain in terms of $E_{b}/N_{0}$ over the system that uses random coding solver alone and 1 dB gain over the serial concatenation of the random coding solver and the ECC decoder, for a message length of 30 to achieve 10−3 frame-error rate. IPA also provides 4 dB gain to achieve 10−3 frame-error rate over the random coding solver alone, for a message length of 128.