Channel State Prediction, Feedback and Scheduling for a Multiuser MIMO-OFDM Downlink

TL;DR

This paper investigates channel prediction, feedback, and user scheduling in multiuser MIMO-OFDM downlink systems, demonstrating that accurate prediction and adaptive scheduling can maintain high performance even with high user mobility.

Contribution

It introduces a realistic channel prediction approach using ESPRIT, analyzes its limitations, and proposes a modified proportional fair scheduling method that accounts for user predictability.

Findings

ESPRIT-based prediction accurately estimates channels at high mobility

Prediction fails for channels with high Doppler spread and clustered angular spread

Modified PFS improves performance by considering user predictability

Abstract

We consider the downlink of a MIMO-OFDM wireless systems where the base-station (BS) has M antennas and serves K single-antenna user terminals (UT) with K larger than or equal to M. Users estimate their channel vectors from common downlink pilot symbols and feed back a prediction, which is used by the BS to compute the linear beamforming matrix for the next time slot and to select the users to be served according to the proportional fair scheduling (PFS) algorithm. We consider a realistic physical channel model used as a benchmark in standardization and some alternatives for the channel estimation and prediction scheme. We show that a parametric method based on ESPRIT is able to accurately predict the channel even for relatively high user mobility. However, there exists a class of channels characterized by large Doppler spread (high mobility) and clustered angular spread for which prediction is intrinsically difficult and all considered methods fail. We propose a modified PFS that take into account the "predictability" state of the UTs, and significantly outperform the classical PFS in the presence of prediction errors. The main conclusion of this work is that multiuser MIMO downlink yields very good performance even in the presence of high mobility users, provided that the nonpredictable users are handled appropriately