Low Complexity Delay-Constrained Beamforming for Multi-User MIMO Systems with Imperfect CSIT

TL;DR

This paper introduces a low-complexity, delay-constrained beamforming method for multi-user MIMO systems with imperfect CSIT, using approximate value functions and convex optimization to improve performance.

Contribution

It develops a novel approximate value function and a tractable optimization framework for delay-constrained MU-MIMO beamforming with imperfect CSIT.

Findings

Significant performance gains over baseline methods.

Effective approximation of the value function and PER.

Convex reformulation enables efficient solution.

Abstract

In this paper, we consider the delay-constrained beamforming control for downlink multi-user MIMO (MU- MIMO) systems with imperfect channel state information at the transmitter (CSIT). The delay-constrained control problem is formulated as an infinite horizon average cost partially observed Markov decision process. To deal with the curse of dimensionality, we introduce a virtual continuous time system and derive a closed-form approximate value function using perturbation analysis w.r.t. the CSIT errors. To deal with the challenge of the conditional packet error rate (PER), we build a tractable closed- form approximation using a Bernstein-type inequality. Based on the closed-form approximations of the relative value function and the conditional PER, we propose a conservative formulation of the original beamforming control problem. The conservative problem is non-convex and we transform it into a convex problem using the semidefinite relaxation (SDR) technique. We then propose an alternating iterative algorithm to solve the SDR problem. Finally, the proposed scheme is compared with various baselines through simulations and it is shown that significant performance gain can be achieved.