Energy-Efficient Power Allocation for Cell-Free Massive MIMO Systems
TL;DR
Validation of the APG algorithm's resilience revealed that it outperformed benchmark algorithms in terms of energy efficiency and execution time, demonstrating its usefulness for challenging optimization tasks, particularly those involving bursty communication.
Abstract
The study investigates a power allocation model for cell-free massive Multiple Input Multiple Output (CF-mMIMO) systems using the Accelerated Proximal Gradient (APG) algorithm.CF- mMIMO systems are a crucial component of sixth-generation networks (6G) that aim to improve system energy efficiency. Unlike conventional optimization approaches that rely on computationally intensive convex relaxation or second-order solvers, the proposed method directly addresses the inherently non-convex fractional energy-efficiency maximization problem under Quality of Service (QoS) and access point (AP) power constraints. A differentiable quadratic penalty mechanism is introduced to seamlessly incorporate the QoS constraints into the objective function, thereby transforming the constrained non-convex problem into a tractable penalized optimization problem suitable for first-order iterative optimization. Validation of the APG algorithm's resilience revealed that it outperformed benchmark algorithms in terms of energy efficiency and execution time, demonstrating its usefulness for challenging optimization tasks, particularly those involving bursty communication. Furthermore, the APG technique guarantees that no penalty functions (PFs) are broken, ensuring that the overall loss converges to zero, irrespective of the beginning conditions.