Optimal Training for Non-Feedback Adaptive PSAM over Time-Varying Rayleigh Fading Channels

TL;DR

This paper investigates optimal training strategies for non-feedback adaptive PSAM in time-varying Rayleigh fading channels, analyzing how training duration and power allocation affect achievable rates under different channel models.

Contribution

It introduces a modular method for rate computation and analyzes four transmission policies to determine optimal training parameters based on channel conditions.

Findings

Placing all power on the last pilot symbol is optimal for causal estimation.

Optimal training varies with channel correlation models and transmission policies.

Achievable rates depend on training duration and power allocation strategies.

Abstract

Time-varying fast fading channels present a major challenge in the design of wireless communication systems. Pilot Symbol Assisted Modulation (PSAM) has been introduced to mitigate the effects of fading and allow coherent demodulation. Our work studies the performance of \emph{non-feedback} adaptive PSAM scheme over time-varying Rayleigh fading channels. A modular method is introduced for computing the rates in an efficient manner. Moreover, four transmission policies are analyzed and we show how optimal training in terms of duration and power allocation varies with the channel conditions and from one transmission policy to another. The performance of these schemes is measured in terms of achievable rates using binary signaling. We formally show that, for a causal estimation, placing all the power on the last pilot symbol is expected to be optimal. Furthermore, the autocorrelation of the fading process is based either on a stationary first order Gauss-Markov modeling of the process or on Jakes' model when higher orders of correlation are analyzed.