Age-Energy Efficiency in WPCNs: A Deep Reinforcement Learning Approach

This paper proposes a deep reinforcement learning (DRL)-based solution framework to maximize the age-energy efficiency (AEE), i.e., the achievable age of information (AoI) gain per consumed energy, in a wireless powered communication network (WPCN), where an edge node (EN) first charges sensors and then the sensors transmit their sensed data to controllers via the EN. To maximize the system AEE, an optimization problem is formulated by jointly optimizing the sensors scheduling and the EN’s transmit power, which is modeled by a Markov decision process via wise definitions of state space, action space, and reward. Simulation results show that compared with the random scheduling-based method, our proposed DRL-based framework improves the AEE by about 4 times when the number of sensors is 30, and it is also shown that the more the number of sensors, the larger the AEE performance can be improved.