Deep Reinforcement Learning Based Uplink Resource Allocation in Open RAN Systems

As the complexity of next-generation mobile communication networks increases, resource allocation becomes one of the fundamental technologies to make a competitive advantage among vendors. Artificial intelligence (AI), particularly reinforcement learning (RL), provides significant opportunities for improvements in this domain. Additionally, the open radio access network (O-RAN) architecture allows seamless integration of AI capabilities into networks through rApp and xApp concepts. This chapter presents two deep reinforcement learning (DRL)-based uplink resource allocation methods, where the first method focuses on maximizing the throughput metrics, while the second one balances the trade-off between throughput and fairness metrics. In the O-RAN architecture, multiple trained DRL models can be orchestrated by rApp policies to dynamically allocate the limited radio resources according to time-varying application requirements. The simulation results demonstrate that the DRL-based uplink resource allocation methods can be effectively utilized to meet different performance objectives specified by high-level policies.