Training and Feedback Optimization for Multiuser MIMO Downlink

TL;DR

This paper optimizes training and feedback strategies for multiuser MIMO downlink systems, balancing channel estimation overhead and feedback methods to maximize throughput.

Contribution

It introduces a closed-form lower bound on ergodic rate considering CSIT errors and compares digital versus analog feedback for system optimization.

Findings

Digital feedback significantly outperforms analog feedback.

Optimal training and feedback parameters depend on system model.

Guidelines for system design based on realistic channel models.

Abstract

We consider a MIMO fading broadcast channel where the fading channel coefficients are constant over time-frequency blocks that span a coherent time $\times$ a coherence bandwidth. In closed-loop systems, channel state information at transmitter (CSIT) is acquired by the downlink training sent by the base station and an explicit feedback from each user terminal. In open-loop systems, CSIT is obtained by exploiting uplink training and channel reciprocity. We use a tight closed-form lower bound on the ergodic achievable rate in the presence of CSIT errors in order to optimize the overall system throughput, by taking explicitly into account the overhead due to channel estimation and channel state feedback. Based on three time-frequency block models inspired by actual systems, we provide some useful guidelines for the overall system optimization. In particular, digital (quantized) feedback is found to offer a substantial advantage over analog (unquantized) feedback.