Joint AP-UE Association and Power Factor Optimization for Distributed Massive MIMO

TL;DR

The paper tackles uplink sum-throughput optimization in distributed mMIMO by jointly optimizing AP-UE association and uplink power control under QoS constraints. It models the per-user SE as $SE^{u}_t = w \log_2(1+\Gamma^{*}_t)$ and uses an $l_1$-penalty term on the association matrix to balance spectral efficiency and front-haul load. An iterative algorithm based on fractional programming, Lagrangian duality, and a quadratic transformation provides convergence guarantees while solving convex subproblems in alternating fashion. Numerical results show substantial sum-SE gains for the joint approach and demonstrate effective front-haul load reduction controlled by the penalty parameter, along with notable improvements in the 90th-percentile per-user SE.

Abstract

The uplink sum-throughput of distributed massive multiple-input-multiple-output (mMIMO) networks depends majorly on Access point (AP)-User Equipment (UE) association and power control. The AP-UE association and power control both are important problems in their own right in distributed mMIMO networks to improve scalability and reduce front-haul load of the network, and to enhance the system performance by mitigating the interference and boosting the desired signals, respectively. Unlike previous studies, which focused primarily on addressing these two problems separately, this work addresses the uplink sum-throughput maximization problem in distributed mMIMO networks by solving the joint AP-UE association and power control problem, while maintaining Quality-of-Service (QoS) requirements for each UE. To improve scalability, we present an l1-penalty function that delicately balances the trade-off between spectral efficiency (SE) and front-haul signaling load. Our proposed methodology leverages fractional programming, Lagrangian dual formation, and penalty functions to provide an elegant and effective iterative solution with guaranteed convergence. Extensive numerical simulations validate the efficacy of the proposed technique for maximizing sum-throughput while considering the joint AP-UE association and power control problem, demonstrating its superiority over approaches that address these problems individually. Furthermore, the results show that the introduced penalty function can help us effectively control the maximum front-haul load.

Figures (6)

• Figure 1: The variation of maximum fronthaul load on an AP, objective function, and the sum SE, when $M=100$.
• Figure 2: The variation of maximum front-haul load on an AP, objective function, and the sum SE, when $M=150$.
• Figure 3: The variation of the sum SE for different scenarios, when $\alpha =0.001$.
• Figure 4: CDF vs per-UE SE for different scenarios, when $\alpha =0.001$.
• Figure 5: The variation of SE with and without discretization of elements of D.