An intelligent wireless channel allocation in HAPS 5G communication system based on reinforcement learning

Abstract

Abstract Channel allocation is the prerequisite for the HAPS (high-altitude platform station) 5G communication network to transmit information. An intelligent wireless channel allocation algorithm for HAPS 5G massive MIMO (multiple-input multiple-output) system based on reinforcement learning was proposed. Q-learning reinforcement learning algorithm and the back-propagation neural network were combined, which enabled HAPS 5G massive MIMO systems autonomous learn according to the environment and allocate channel resources of the system efficiently. Agents perceive the state information in the channel environment through continuous interaction with the channel environment, learn from the environment state to the action mapping, and use the back-propagation neural network for learning training, use the neural network instead of the Q value table, and train the network with each Q update as a training example to update the evaluation function. It effectively improves the overall performance of the system.

Keywords

• HAPS
• 5G
• Massive MIMO
• Channel allocation
• Reinforcement learning