Resource Allocation for Network Slicing in Open RAN: A Hierarchical Learning Approach
Kai Qiao, Hongchao Wang, Weiting Zhang, Dong Yang, Yuming Zhang, Ning Zhang · IEEE Transactions on Cognitive Communications and Networking · 2025
Network slicing technology and the Open Radio Access Network (O-RAN) architecture provide solutions for resource isolation and dynamic resource optimization in resource allocation models for heterogeneous services. However, the existing resource allocation models employ uniformly sized radio Resource Blocks (RBs) and fixed-length time slots, which incur low resource utilization and a high packet loss rate, especially when the packet arrival rate is high. In this paper, we propose a multi-cell, multi-dimensional resource, and multi-timescale O-RAN slicing framework, named M3O-RANS, which meets the Quality of Service (QoS) requirements of heterogeneous services and reduces resource costs for network service providers. Specifically, the M3O-RANS framework partitions multiple timescales into a large timescale and multiple small timescales, using radio RBs of different sizes for inter-slice operations at the large timescale and reusing radio RBs across the multi-cell network for intra-slice operations at the small timescale, thereby improving resource utilization. To optimally allocate radio and computing resources according to time-varying network conditions, we formulate an optimization problem with the objective of minimizing resource costs for network service providers while satisfying delay and reliability requirements. Since inter-slice and intra-slice resource constraints are mutually coupled, it is difficult to solve the problem via traditional Deep Reinforcement Learning (DRL) methods. Therefore, we decompose the original problem into two subproblems and propose a Parallel Hierarchical DRL-based Resource Allocation (PHDRA) algorithm. The proposed algorithm can make decisions regarding the size and number of radio RBs and the amount of computing resources at the upper layer while making radio RB selection and computing resource allocation decisions at the lower layer. Simulation results demonstrate that the PHDRA algorithm can improve the QoS of heterogeneous services and reduce resource costs for network service providers compared with state-of-the-art benchmarks.