A Robust Decoding Scheme for Convolutionally Coded Transmission Through a Markov Gaussian Channel

Abstract

Communication systems are susceptible to impulse noise, particularly when the impulse statistics are not time invariant and are difficult to model accurately. To address the challenge of impulse noise, a robust and efficient decoding scheme was devised for single-carrier convolutionally coded transmissions over memory impulse noise channels. By accommodating channel states but without relying on statistical knowledge of impulses, the Viterbi algorithm (VA) based on an expanded set of trellis states was employed to perform maximum-likelihood decoding. A detailed analysis of complexity was offered; the analytical results reinforced the efficiency of the proposed scheme compared with the traditional VA. The simulation results indicated that the proposed decoding scheme is compellingly robust: The bit error probability performance level attained using the proposed decoder is remarkably close to that of an optimal decoder, which uses impulse statistics; furthermore, the proposed decoder was superior to the alpha-penalty function decoder, which neglects the channel memory property and experiences an error floor under fairly general circumstances.