AoI minimization for Uplink Cell-Free Networks:A DRL-Based Multi-Objective Approach

In this paper, we propose a deep reinforcement learning (DRL)-based framework to jointly minimize the ergodic age of information (AoI) and the total transmit power in an uplink (UL) cell-free massive multiple-input multiple-output (CF-mMIMO) system. In particular, the multiple-objective resource allocation problem is formulated into an optimization problem subject to quality-of-service (QoS) and maximum transmit power constraints. Due to the long-term nature of the problem, it is challenging to solve using conventional convex optimization techniques. Therefore, the problem is reformulated as a reinforcement learning (RL) environment and a novel state space and reward function are developed. Finally, the soft actor-critic DRL agent is developed to solve the reformulated problem. Simulation results demonstrate that the proposed scheme achieves significant power savings while maintaining a relatively low average AoI score compared to the benchmark schemes.