Adaptive Pufferfish Based Network Slicing and Scheduling Control in O-RAN for Next Generation Cellular Networks

The cellular networks of the next generation will be dependent on virtualized, programmable, and disaggregated architectures and can be entirely cloud-based. Cellular networks are being made by the Open Radio Access Network (O-RAN) prototype and the reference design offered through O-RAN Alliance. In this article, a unique Deep Reinforcement Learning (DRL) agent design that can learn control policies with diverse minimum performance needs and different Service Level Agreements (SLAs) in O-RAN applications is proposed for next generation cellular networks. This method concentrates on RAN slicing and SLAs that define the highest acceptable end-to-end latency levels. For optimal scheduling, pufferfish optimization algorithm (POA) is used. The proposed work has conducted the experiments on Colosseum, the biggest wireless network emulator in the world, to show real-time analytics. Utilizing the O-RAN open interfaces to gather data at the network's edge, the viability of RAN control using xApps operating is demonstrated on the near real-time RAN Intelligent Controller for optimizing coexisting network slices' scheduling policies.