Joint Access Configuration and Beamforming for Cell-Free Massive MIMO Systems With Dynamic TDD

Abstract

We address the trade-off between system throughput and user equipment (UE) fairness in dynamic time division duplex (TDD) cell-free (CF)-massive multiple-input multiple-output (mMIMO) systems, developing to that end a joint access point (AP) access configuration, power allocation, and beamforming design scheme. Unlike state-of-the-art (SotA) methods, which mostly focus on sum-rate maximization or max-min worst case optimization, our design approach is to maximize the geometric mean of the UEs’ throughput performance under a constrained transmit power, which is shown to yield an excellent compromise between system throughput and user fairness. The direct reformulation of the resulting optimization problem is, however, hard to solve due not only to the non-convexity of the objective function, but also to the binary constraint induced by the AP access configuration. We thus present an efficient ( $i.e$ ., polynomial time complexity) solution for the problem via a combination of fractional programming (FP) and convex–concave procedure (CCP), assisted by a negative entropy regularizer that promotes a binary solution. Numerical simulations are offered to evaluate the throughput performance and fairness index of the proposed algorithm in comparison not only to conventional TDD-based solutions, but also to recent dynamic TDD SotA designs, which illustrate the effectiveness of the proposed approach over existing methodologies both in throughput and fairness.

Keywords

• Cell-free massive multiple-input multiple-output
• convex optimization
• convex-concave procedure
• dynamic time-division duplex
• geometric mean
• fractional programming