Efficient 5G Resource Block Scheduling Using Action Branching and Transformer Networks

This paper presents a deep reinforcement learning-based scheduling solution tailored for 5G networks. The proposed neural network architecture, utilizing an encoder-only transformer and action branching, is designed to handle large action spaces for resource block allocation in wireless environments. By training on variable number of user equipment scenarios, the solution generalizes well across different configurations. Experimental results in Nokia’s wireless suite environment demonstrate superior performance in packet loss, compared to heuristics.