Highly Accelerated Weighted MMSE Algorithms for Designing Precoders in FDD Systems with Incomplete CSI

TL;DR

This paper introduces highly accelerated weighted MMSE algorithms for precoder design in FDD MISO systems with incomplete CSI, significantly reducing computation time while maintaining performance in limited channel knowledge scenarios.

Contribution

It develops a novel, fast iterative precoder design method with closed-form steps and an improved stochastic WMMSE approach for FDD systems with scarce CSI.

Findings

Effective in scenarios with limited pilots and channel knowledge

Reduces computation time of precoder design algorithms

Maintains high sum rate performance in challenging conditions

Abstract

In this work, we derive a lower bound on the training-based achievable downlink (DL) sum rate (SR) of a multi-user multiple-input-single-output (MISO) system operating in frequency-division-duplex (FDD) mode. Assuming linear minimum mean square error (LMMSE) channel estimation is used, we establish a connection of the derived lower bound on the signal-to-interference-noise-ratio (SINR) to an average MSE that allows to reformulate the SR maximization problem as the minimization of the augmented weighted average MSE (AWAMSE). We propose an iterative precoder design with three alternating steps, all given in closed form, drastically reducing the computation time. We show numerically the effectiveness of the proposed approach in challenging scenarios with limited channel knowledge, i.e., we consider scenarios with a very limited number of pilots. We additionally propose a more efficient version of the well-known stochastic iterative WMMSE (SIWMMSE) approach, where the precoder update is given in closed form.