Joint Beamforming for Multiaccess MIMO Systems with Finite Rate Feedback

TL;DR

This paper introduces a joint quantization and feedback strategy for multiaccess MIMO systems with finite rate feedback, improving sum rate performance by leveraging vector quantization and user selection analysis.

Contribution

It proposes a novel joint quantization and feedback scheme that outperforms individual strategies, with analytical performance characterization using extreme order statistics and random matrix theory.

Findings

The joint strategy enhances sum rate compared to individual quantization.

Performance is analytically characterized using extreme order statistics.

The scheme serves as a benchmark for finite rate feedback in multiaccess MIMO systems.

Abstract

This paper considers multiaccess multiple-input multiple-output (MIMO) systems with finite rate feedback. The goal is to understand how to efficiently employ the given finite feedback resource to maximize the sum rate by characterizing the performance analytically. Towards this, we propose a joint quantization and feedback strategy: the base station selects the strongest users, jointly quantizes their strongest eigen-channel vectors and broadcasts a common feedback to all the users. This joint strategy is different from an individual strategy, in which quantization and feedback are performed across users independently, and it improves upon the individual strategy in the same way that vector quantization improves upon scalar quantization. In our proposed strategy, the effect of user selection is analyzed by extreme order statistics, while the effect of joint quantization is quantified by what we term ``the composite Grassmann manifold''. The achievable sum rate is then estimated by random matrix theory. Due to its simple implementation and solid performance analysis, the proposed scheme provides a benchmark for multiaccess MIMO systems with finite rate feedback.