2022/02/25 by Tianlun Hu, Hu, Tianlun, Qi Liao +7 · 2 citations
Computer Science · Engineering · #FOS: Computer and information sciences #Ferroelectric and Negative Capacitance Devices #Multiagent Systems (cs.MA) #Networking and Internet Architecture (cs.NI) #Software System Performance and Reliability #Software-Defined Networks and 5G
paper · pdf · doi:10.48550/arxiv.2202.12833
openalex publication_date 2022/02/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Network slicing enables the operator to configure virtual network instances for diverse services with specific requirements. To achieve the slice-aware radio resource scheduling, dynamic slicing resource partitioning is needed to orchestrate multi-cell slice resources and mitigate inter-cell interference. It is, however, challenging to derive the analytical solutions due to the complex inter-cell interdependencies, interslice resource constraints, and service-specific requirements. In this paper, we propose a multi-agent deep reinforcement learning (DRL) approach that improves the max-min slice performance while maintaining the constraints of resource capacity. We design two coordination schemes to allow distributed agents to coordinate and mitigate inter-cell interference. The proposed approach is extensively evaluated in a system-level simulator. The numerical results show that the proposed approach with inter-agent coordination outperforms the centralized approach in terms of delay and convergence. The proposed approach improves more than two-fold increase in resource efficiency as compared to the baseline approach.