Convolutionally Coded Transmission over Markov-Gaussian Channels: Analysis and Decoding Metrics

It has been widely acknowledged that the aggregate interference at the receiver for various practical communication channels can often deviate markedly from the classical additive white Gaussian noise (AWGN) assumption due to various ambient phenomena. Moreover, the physical nature of the underlying interference generating process in such cases can lead to a bursty behaviour of the interfering signal, implying that it is highly likely that consecutive symbols are affected by similar noise levels. In this paper, we devise and analyze detection techniques, in conjunction with a convolution code, for such interference channels that possess non-negligible memory by considering optimum and sub-optimum decoding metrics. In particular the inherent memory in the noise process is modeled as a first-order Markov chain, whose state selects the variance of the instantaneous Gaussian noise, leading to a Markov-Gaussian channel model. Analytical expressions are obtained for the cutoff rate, which is...

• Type: Journal
• Year: 2010
• Where: TCOM
• Authors: Jeebak Mitra, Lutz H.-J. Lampe
• Keywords: Additive White Gaussian Noise, Gaussian Noise, Instantaneous Gaussian Noise, Software Engineering, TCOM 2010