Energy efficient resource allocation method for 5G access network based on reinforcement learning algorithm

Abstract

Edge computing and IIoT (Industrial Internet of Things) are two representative application scenarios in 5G (5th Generation) mobile communication technology network. Therefore, this paper studies the resource allocation methods for these two typical scenarios, and proposes an energy-efficient resource allocation method by using DRL algorithm, which enhances the network performance and reduces the network operation cost. Firstly, according to the characteristics of edge computing, taking the connection relationship between base stations, users and the transmission power allocated by base stations to users as decision variables, minimizing the overall energy efficiency as the goal, and taking the needs of mobile users as constraints, a resource allocation model for user quality of service (QoS) guarantee is designed. Secondly, simulation experiments are used to verify that the proposed method has faster training speed and convergence speed, ensures the quality-of-service requirements of mobile users, and realizes intelligent and energy-saving resource allocation. The approximate amounts of steps to convergence under four cases are 500, 350, 250, and 220, respectively, and the values of awards are 21.76, 21.09, 20.38, and 20.25, respectively. Thirdly, aiming at the IIoT environment scenario, this paper proposes a resource allocation method for 5G wide connection low delay services based on asynchronous dominant action evaluation algorithm (A3C). Firstly, according to the characteristics of IIoT environment, taking the connection relationship between base stations and users and the transmission power allocated by base stations to users as decision variables and maximizing energy efficiency while satisfying needs of each user as optimization goal, a resource allocation model for 5G wide connection low delay services is designed. On this basis, an energy-efficient resource allocation method according to A3C is proposed. The hierarchical aggregation clustering algorithm (HAC) is adopted to determine the connection relationship between the base station and users, and the A3C algorithm is adopted to allocate transmission power to users, so as to maximize the overall energy efficiency and ensure the needs of each user.