Safe-NORA: Safe Reinforcement Learning-based Mobile Network Resource Allocation for Diverse User Demands
Wenzhen Huang, Tong Li, Yuting Cao, Z Q Lyu, Yanping Liang, Li Yu, Depeng Jin, Junge Zhang, Yong Li
Abstract
As mobile communication technologies advance, mobile networks become increasingly complex, and user requirements become increasingly diverse. To satisfy the diverse demands of users while improving the overall performance of the network system, the limited wireless network resources should be efficiently and dynamically allocated to them based on the magnitude of their demands and their relative location to the base stations. We separated the problem into four constrained subproblems, which we then solved using a safe reinforcement learning method. In addition, we design a reward mechanism to encourage agent cooperation in distributed training environments. We test our methodology in a simulated scenario with thousands of users and hundreds of base stations. According to experimental findings, our method guarantees that over 95% of user demands are satisfied while also maximizing the overall system throughput.