Abstract
In the fifth generation (5G) wireless communication network, to better offer service for different vertical industry applications, network slicing is needed to meet the differential requirements. In this paper, we study the joint problem of radio resource allocation and power resource allocation in the radio access network (RAN) to improve the radio resource utilization with multi-user demands in smart grid scenario. Different with conventional single-agent algorithm, we propose a non-orthogonal multiple access (NOMA) based multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve this dynamic optimization problem. The scheme determines the remote radio unit (RRU) and physical resource block (PRB) allocation for each user, and updates the neural network to dynamically adjust the resource allocation strategy to maximize the long-term rate of network slices. The reliability and latency requirements of network slicing are guaranteed as well. Simulation results show the convergence and efficiency of the proposed algorithm.
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