Power Allocation in Orthogonal Frequency Division Multiplexing-based Wireless Networks Using Reinforcement Learning

Energy efficiency is one of the most essential requirements for wireless sensor networks and the internet of things, since batteries are usually the main source of power for these networks. Appropriate techniques based on artificial intelligence can reduce power consumption and provide the required quality of service parameters for the network. In this paper, we approach the challenging problem of allocation of signal transmission power based on the orthogonal frequency division multiplexing technique. We propose to use reinforcement learning-based algorithms to find the optimal policy for allocating power to wireless network devices using a reward function. More specifically, we propose to use the double deep Q-network (DDQN) agent due to its higher learning capacity compared to the Q-learning, deep Q-network (DQN), and the distributed algorithm (DA), a classic algorithm in the literature for power allocation. Simulation results show that the DDQN agent presents promising solutions for power allocation in wireless networks.