Energy Optimization across Training and Data for Multiuser Minimum Sum-MSE Linear Precoding

TL;DR

This paper derives optimal energy allocation strategies for training and data phases in multiuser downlink systems with imperfect channel information, enhancing transceiver performance through a closed-form solution.

Contribution

It introduces a novel closed-form energy allocation method for joint training and data phases in multiuser MSE minimization, considering imperfect channel estimates.

Findings

Optimal energy allocation improves system performance.

Closed-form solution simplifies implementation.

Simulation confirms performance gains.

Abstract

This paper considers minimum sum mean-squared error (sum-MSE) linear transceiver designs in multiuser downlink systems with imperfect channel state information. Specifically, we derive the optimal energy allocations for training and data phases for such a system. Under MMSE estimation of uncorrelated Rayleigh block fading channels with equal average powers, we prove the separability of the energy allocation and transceiver design optimization problems. A closed-form optimum energy allocation is derived and applied to existing transceiver designs. Analysis and simulation results demonstrate the improvements that can be realized with the proposed design.