Joint Power

Joint power allocation aims to optimize the distribution of power across multiple users or devices in wireless communication systems, maximizing overall network performance metrics like data rate and spectral efficiency. Current research heavily utilizes deep reinforcement learning (DRL), including variations like Double Deep Q-Networks (DDQN) and Soft Actor-Critic (SAC), to address the complex, non-convex optimization problems inherent in this task, often coupled with beamforming techniques for improved signal directionality. These advancements are crucial for enabling efficient resource management in emerging 5G and beyond networks, particularly in scenarios like integrated access and backhauling and federated learning, where energy constraints and heterogeneous channel conditions pose significant challenges.