A Unified Joint Optimization of Training Sequences and Transceivers Based on Matrix-Monotonic Optimization

TL;DR

This paper introduces a unified framework for jointly optimizing training sequences and transceivers in MIMO systems using matrix-monotonic optimization, improving system performance with explicit design relationships.

Contribution

It proposes a novel joint matrix-monotonic optimization framework for MIMO transceiver and training sequence design considering various metrics and CSI scenarios.

Findings

Optimal matrix structures derived for both CSI scenarios

Joint optimization improves system performance

Numerical results validate theoretical benefits

Abstract

Channel estimation and data transmission constitute the most fundamental functional modules of multiple-input multiple-output (MIMO) communication systems. The underlying key tasks corresponding to these modules are training sequence optimization and transceiver optimization. Hence, we jointly optimize the linear transmit precoder and the training sequence of MIMO systems using the metrics of their effective mutual information (MI), effective mean squared error (MSE), effective weighted MI, effective weighted MSE, as well as their effective generic Schur-convex and Schur-concave functions. Both statistical channel state information (CSI) and estimated CSI are considered at the transmitter in the joint optimization. A unified framework termed as joint matrix-monotonic optimization is proposed. Based on this, the optimal precoder matrix and training matrix structures can be derived for both CSI scenarios. Then, based on the optimal matrix structures, our linear transceivers and their training sequences can be jointly optimized. Compared to state-of-the-art benchmark algorithms, the proposed algorithms visualize the bold explicit relationships between the attainable system performance of our linear transceivers conceived and their training sequences, leading to implementation ready recipes. Finally, several numerical results are provided, which corroborate our theoretical results and demonstrate the compelling benefits of our proposed pilot-aided MIMO solutions.