2024/11/13 by Petteri Kela, Kela, Petteri, Bryan Liu +3
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Network Optimization #FOS: Electrical engineering #Signal Processing (eess.SP) #Wireless Communication Networks Research #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2411.08529
openalex publication_date 2024/11/13 · openalex created_date 2024/11/16 · openalex updated_date 2026/07/29
Efficient radio packet scheduling remains one of the most challenging tasks in cellular networks, and while heuristic methods exist, practical deep learning-based schedulers that are 3GPP-compliant and capable of real-time operation in 5G and beyond are still missing. To address this, we first take a critical look at previous deep scheduler efforts. Secondly, we enhance State-of-the-Art (SoTA) deep Reinforcement Learning (RL) algorithms and adapt them to train our deep scheduler. In particular, we propose a novel combination of training techniques for Proximal Policy Optimization (PPO) and a new Distributional Soft Actor-Critic Discrete (DSACD) algorithm, which outperformed other variants tested. These improvements were achieved while maintaining minimal actor network complexity, making them suitable for real-time computing environments. Furthermore, entropy learning in SACD was fine-tuned to accommodate resource allocation action spaces of varying sizes. Our proposed deep schedulers exhibited strong generalization across different bandwidths, number of Multi-User MIMO (MU-MIMO) layers, and traffic models. Ultimately, we show that our pre-trained deep schedulers outperform their heuristic rivals in realistic and standard-compliant 5G system-level simulations.