Reinforcement learning-based hybrid spectrum resource allocation scheme for the high load of URLLC services

Ultra-reliable and low-latency communication (URLLC) in mobile networks is still one of the core solutions that require thorough research in 5G and beyond. With the vigorous development of various emerging URLLC technologies, resource shortages will soon occur even in mmWave cells with rich spectrum resources. As a result of the large radio resource space of mmWave, traditional real-time resource scheduling decisions can cause serious delays. Consequently, we investigate a delay minimization problem with the spectrum and power constraints in the mmWave hybrid access network. To reduce the delay caused by high load and radio resource shortage, a hybrid spectrum and power resource allocation scheme based on reinforcement learning (RL) is proposed. We compress the state space and the action space by temporarily dumping and decomposing the action. The multipath deep neural network and policy gradient method are used, respectively, as the approximater and update method of the parameterized policy. The experimental results reveal that the RL-based hybrid spectrum and the power resource allocation scheme eventually converged after a limited number of iterative learnings. Compared with other schemes, the RL-based scheme can effectively guarantee the URLLC delay constraint when the load does not exceed 130%.