Channel Estimation and Linear Precoding in Multiuser Multiple-Antenna TDD Systems

TL;DR

This paper investigates the interplay between channel estimation and linear precoding in multiuser MIMO TDD systems, accounting for training overhead and estimation errors to optimize system throughput.

Contribution

It introduces precoding techniques that incorporate channel estimation errors and derives bounds to evaluate their performance, highlighting the coupling of estimation and precoding.

Findings

Precoding methods considering estimation errors improve throughput.

Derived bounds closely match actual performance in typical scenarios.

Explicitly modeling training overhead enhances system analysis accuracy.

Abstract

Traditional approaches in the analysis of downlink systems decouple the precoding and the channel estimation problems. However, in cellular systems with mobile users, these two problems are in fact tightly coupled. In this paper, this coupling is explicitly studied by accounting for channel training overhead and estimation error while determining the overall system throughput. The paper studies the problem of utilizing imperfect channel estimates for efficient linear precoding and user selection. It presents precoding methods that take into account the degree of channel estimation error. Information-theoretic lower and upper bounds are derived to evaluate the performance of these precoding methods. In typical scenarios, these bounds are close.