Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Precoding and Signal Shaping for Digital Transmission
Precoding and Signal Shaping for Digital Transmission
Coding for Wireless Channels (Information Technology: Transmission, Processing and Storage)
Coding for Wireless Channels (Information Technology: Transmission, Processing and Storage)
Robust Tomlinson–Harashima Precoding for the Wireless Broadcast Channel
IEEE Transactions on Signal Processing
Gaussian class multivariate Weibull distributions: theory and applications in fading channels
IEEE Transactions on Information Theory
Tomlinson-Harashima Precoding for Broadcast Channels with Uncertainty
IEEE Journal on Selected Areas in Communications
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Transmitter precoding strategies in broadcast systems generally assume perfect knowledge of channel state information (CSI) at the transmitter. In this paper, we study linear precoding with arbitrary error in the CSI from an estimation theoretic point of view. We derive a Bayesian Cramér-Rao type bound on the sum mean squared error (SMSE) achievable at the receiver for any linear precoding scheme for arbitrary feedback noise and channel fading. We next specialize this result to power constrained precoders. It is shown that the regularity conditions of the bound may be significantly weakened for power constrained precoders. Interestingly, we obtain a bound whose validity depends on rather weak conditions of continuity and differentiability on the joint distribution of the channel and feedback. We demonstrate the bound by applying it to Gaussian, Nakagami-m and Weibull fading models, with the assumption of feedback corrupted by additive Gaussian noise.