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Random Coding Error Exponents for the Two-User Interference Channel
This paper is about deriving lower bounds on the error exponents for the
two-user interference channel under the random coding regime for several
ensembles. Specifically, we first analyze the standard random coding ensemble,
where the codebooks are comprised of independently and identically distributed
(i.i.d.) codewords. For this ensemble, we focus on optimum decoding, which is
in contrast to other, suboptimal decoding rules that have been used in the
literature (e.g., joint typicality decoding, treating interference as noise,
etc.). The fact that the interfering signal is a codeword, rather than an
i.i.d. noise process, complicates the application of conventional techniques of
performance analysis of the optimum decoder. Also, unfortunately, these
conventional techniques result in loose bounds. Using analytical tools rooted
in statistical physics, as well as advanced union bounds, we derive
single-letter formulas for the random coding error exponents. We compare our
results with the best known lower bound on the error exponent, and show that
our exponents can be strictly better. Then, in the second part of this paper,
we consider more complicated coding ensembles, and find a lower bound on the
error exponent associated with the celebrated Han-Kobayashi (HK) random coding
ensemble, which is based on superposition coding.Comment: accepted IEEE Transactions on Information Theor
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