A Novel MADDPG Algorithm for Efficient Power Allocation in Cell-Free Massive MIMO

As we move towards beyond-fifth and sixth-generation wireless networks, new challenges emerge, including the need for higher data rates and lower latency. One promising solution to overcome some of these challenges is cell-free massive MIMO. However, a significant drawback of such systems is the high computational cost, particularly in downlink power allocation operations. This paper introduces a novel approach to tackle this issue by leveraging the proven capabilities of reinforcement learning (RL) algorithms. Specifically, we develop a multi-agent deep deterministic policy gradient (MADDPG) algorithm to maximize the sum spectral efficiency (SE) in power allocation schemes. In addition, a novel evolutionary hyperparameter optimization technique has been applied for the first time in this context. The proposed solution not only outperforms existing methods in the literature but also accurately replicates the behavior of the sum SE maximization approach solved as a convex problem. Notably, it achieves a significant computational efficiency improvement by reducing computation time by a factor of 1,000 compared to the convex solution. The results have been validated in properly configured cell-free networks, addressing the limitations of previous studies that did not consider a sufficiently large number of access points relative to user equipment. This work represents a significant advancement in the field, offering a promising and efficient solution for future wireless communication systems.