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.INDEX TERMS Cell-free massive multiple-input multiple-output, convex optimization, convex-concave procedure, dynamic time-division duplex, geometric mean, fractional programming.

Clustering and Beamforming for User-Centric Cell-Free Massive MIMO With Backhaul Capacity Limitation

“…The joint optimized design method for AP access configuration and beamformers has already been discussed in [51], which seeks the access configuration of CF-mMIMO systems with dynamic time division duplex. The joint optimization methods in literature [51] assume perfect knowledge of CSI. However, this paper considers the channel estimation error and the quantization noise.…”

“…Therefore, the solution to the optimized design problem must search for the worst-case SINR based on CSI held by CPU. The robust design algorithm based on [51] must search for the worst-case SINR in the outer loop, so this application is not realistic from the viewpoint of computational complexity. In this paper, we implement a design assuming that the CSI, including the noise from the channel estimation and the backhaul communication, is the true CSI.…”

Deep Reinforcement Learning Driven Joint Dynamic TDD and RRC Connection Management Scheme in Massive IoT Networks

“…In contrast, Fukue et al [41] highlight the energy constraints of IoT devices as they address the trade-off between system throughput and DL/UL traffic fairness in TDD management of IoT networks. Lee et al [42], [43] conduct a comprehensive study on the coexistence of HTC and MTC, exploring power allocation in beamforming under dynamic TDD management.…”

Can Dynamic TDD Enabled Half-Duplex Cell-Free Massive MIMO Outperform Full-Duplex Cellular Massive MIMO?

“…With this in mind, two potential physical layer solutions have been envisioned: (i) the capability to serve both uplink (UL) and the downlink (DL) UEs using the same time-frequency resources, thereby potentially doubling the sum UL-DL SE over conventional time/frequency division duplexing (T/FDD) systems, and (ii) distributed deployments of the remote radio units or access points (APs) for ubiquitous connectivity and high macrodiversity as opposed to a centralized MIMO cellular system. The key enablers of the above are: (i) the use of fullduplex (FD) APs [1] or the use of dynamic time-division duplexing (DTDD) with half-duplex (HD) APs [2]- [4]; and (ii) cell-free multiple-input multiple-output (CF-MIMO) [5], [6]; respectively. This paper presents a comparative performance analysis of FD and DTDD CF-MIMO systems.…”

“…employs a series of equivalent convex reformulations. Further, closed-form solutions for the power allocation coefficients and associated auxiliary variables are derived using the alternating direction method of multipliers (ADMM) 2 in the case of DL and using an augmented Lagrange multiplier in the case of UL. Also, in the UL, we observe considerable improvement in the proposed SINR optimal combining coupled with UL power control compared to existing benchmarks and when either of these two schemes is applied individually (see Fig.…”