Channel Estimation and Optimal Training Design for Correlated MIMO Two-Way Relay Systems in Colored Environment

TL;DR

This paper develops optimal training and channel estimation methods for correlated MIMO two-way relay systems considering interference, improving accuracy and reducing training overhead through iterative algorithms and structural covariance assumptions.

Contribution

It introduces new iterative training design algorithms and optimal estimation strategies tailored for correlated MIMO TWR systems in colored environments.

Findings

Proposed near-optimal training sequence algorithms.

Derived minimum training length for accurate estimation.

Validated effectiveness through comprehensive simulations.

Abstract

In this paper, while considering the impact of antenna correlation and the interference from neighboring users, we analyze channel estimation and training sequence design for multi-input multi-output (MIMO) two-way relay (TWR) systems. To this end, we propose to decompose the bidirectional transmission links into two phases, i.e., the multiple access (MAC) phase and the broadcasting (BC) phase. By considering the Kronecker-structured channel model, we derive the optimal linear minimum mean-square-error (LMMSE) channel estimators. The corresponding training designs for the MAC and BC phases are then formulated and solved to improve channel estimation accuracy. For the general scenario of training sequence design for both phases, two iterative training design algorithms are proposed that are verified to produce training sequences that result in near optimal channel estimation performance. Furthermore, for specific practical scenarios, where the covariance matrices of the channel or disturbances are of particular structures, the optimal training sequence design guidelines are derived. In order to reduce training overhead, the minimum required training length for channel estimation in both the MAC and BC phases are also derived. Comprehensive simulations are carried out to demonstrate the effectiveness of the proposed training designs.