Universal Decoding for Gaussian MIMO-ISI: Spherical Ensembles and Symmetric Neighborhoods

Abstract

We construct a universal decoder for cyclic Gaussian multiple-input multiple-output channels with intersymbol interference and unknown channel taps and spatial noise covariance. Codewords are drawn independently and uniformly from a sphere of fixed total energy. Under stated bounds on frequency variation, the decoder attains the ensemble-average error exponent of informed maximum-likelihood decoding for fixed channels and channels with growing memory. We approximate the frequency response by a constant matrix within each frequency block. For this auxiliary channel, every matched log-likelihood is affine in a common sufficient statistic. At fixed received output, we compute the statistic density of an independent competitor. The decoder selects the candidate with the smallest statistic density, equivalently minimizing a weighted sum of residual log-determinants. The density and its support volume give an upper bound on the universal competitor probability. Affinity ensures that at least half of a suitable symmetric neighborhood of the transmitted statistic has matched likelihood no smaller than that of the transmitted word. Integrating a density lower bound over this half gives the matched competitor bound. These bounds establish equality of error exponents, which a Gaussian channel comparison transfers to the original channel. The same decoder attains the matched exponent for independent frequency blocks with prescribed energies satisfying the stated conditions. For energies proportional to block length, an additional gain condition ensures that this exponent is at least that of the total-energy spherical ensemble.