We propose a decoding algorithm for tail-biting convolutional codes over phase noise channels. It can be seen as a reduced complexity approximation of maximum likelihood decoding. We target short blocks and extend the wrap-around Viterbi algorithm to trellises describing the random evolution of the phase impairment, for which we adopt two different models: a blockwise non-coherent and a blockwise Wiener channel model. Numerical results show that the performance of the proposed algorithm is within a few tenths of dB or less from maximum likelihood decoding for the setup studied in this letter.
Approximate ML Decoding of Short Convolutional Codes Over Phase Noise Channels / Gaudio, Lorenzo; Matuz, Balazs; Ninacs, Tudor; Colavolpe, Giulio; Vannucci, Armando. - In: IEEE COMMUNICATIONS LETTERS. - ISSN 1089-7798. - 24:2(2020), pp. 325-329. [10.1109/LCOMM.2019.2955730]
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