Multi-Policy Lazy RAN Slicing With Bayesian Optimization for Energy-Efficient B5G ITS
Abstract
Agent-controlled intelligent subnetworks are envisioned as an integral part of Beyond 5G (B5G) communications for automatically selecting distinct policies based on dynamic performance constraints. In the B5G era, energy efficiency will be a prominent feature of Radio Access Network (RAN) slicing for achieving sustainable networks and lowering operational costs while multiplexing users with heterogeneous Quality-of-Service (QoS) requirements. Considering the importance of Intelligent Transportation Systems (ITS) within the realm of B5G applications, in this paper, we propose the LazyRAN framework as an energy-efficient Radio Resource Block (RRB) allocation approach for multi-policy RAN slicing in B5G ITS edge networks. Initially, we focus on resource utilization efficiency and define Lazy Skip Markov Decision Process (LS-MDP) formulation for spectrum agents to individually perform fine and coarse stochastic control depending on performance requirements incorporating varying levels of laziness. Followingly, we propose LazyRAN framework that uses Bayesian Optimization (BO) based Offline Policy Selection (OPS) for optimality calculations in case of multiple slicing policies. Our framework enables efficient multi-policy evaluation employing both exploration and exploitation in the agent policy space. The OPS method utilizes a Gaussian Process (GP) surrogate function combining logged data with online agent interactions before searching for the best slicing policy with BO. Using an energy-aware approach, hybrid QoS reward per energy consumption (HQEC), we compare the performance of LazyRAN framework in centralized and decentralized settings considering diverse agent policies. Our results show that the proposed scheme can significantly improve energy utilization with greater HQEC and higher throughput measurements while satisfying hybrid QoS demands.