MIMO Downlink Scheduling with Non-Perfect Channel State Knowledge

TL;DR

This paper introduces a systematic framework for MIMO downlink scheduling that accounts for imperfect channel state information, distinguishing between predictable and non-predictable users to optimize performance.

Contribution

It presents a novel scheduling scheme that effectively handles non-perfect CSI by leveraging user predictability, outperforming traditional methods under realistic channel models.

Findings

Significant performance improvements over mismatched scheduling schemes.

Effective differentiation between predictable and non-predictable users.

Validation using a realistic 3GPP channel model.

Abstract

Downlink scheduling schemes are well-known and widely investigated under the assumption that the channel state is perfectly known to the scheduler. In the multiuser MIMO (broadcast) case, downlink scheduling in the presence of non-perfect channel state information (CSI) is only scantly treated. In this paper we provide a general framework that addresses the problem systematically. Also, we illuminate the key role played by the channel state prediction error: our scheme treats in a fundamentally different way users with small channel prediction error ("predictable" users) and users with large channel prediction error ("non-predictable" users), and can be interpreted as a near-optimal opportunistic time-sharing strategy between MIMO downlink beamforming to predictable users and space-time coding to nonpredictable users. Our results, based on a realistic MIMO channel model used in 3GPP standardization, show that the proposed algorithms can significantly outperform a conventional "mismatched" scheduling scheme that treats the available CSI as if it was perfect.