On Estimating the Autoregressive Coefficients of Time-Varying Fading Channels

TL;DR

This paper introduces a method for estimating autoregressive coefficients of time-varying MIMO channels, enabling improved channel tracking and prediction in fast fading environments, with proven convergence in large dimensions.

Contribution

The paper proposes a novel estimation method for autoregressive parameters in SIMO channels, demonstrating almost sure convergence and applicability to channel tracking.

Findings

Estimation method converges almost surely in large dimensions.

Method effectively tracks time-varying SIMO channels.

Applicable to fast fading environments.

Abstract

As several previous works have pointed out, the evolution of the wireless channels in multiple input multiple output systems can be advantageously modeled as an autoregressive process. Therefore, estimating the coefficients, and, in particular, the state transition matrix of this autoregressive process is a key to accurate channel estimation, tracking, and prediction in fast fading environments. In this paper we assume a time varying spatially uncorrelated channel, which is approximately the case with proper antenna spacing at the base station in rich scattering environments. We propose a method for autoregressive parameter estimation for the single input multiple output (SIMO) channel. We show an almost sure convergence of the estimated coefficients to the true autoregressive coefficients in large dimensions. We apply the proposed method to SIMO channel tracking.