Intelligent Hierarchical Network Slicing Based on Dynamic Multi-Connectivity in Cell-Free Distributed Massive MIMO Systems

Abstract

In order to provide customized services for the future sixth-generation (6 G) mass business, we propose a two-timescale intelligent radio access network (RAN) slicing scheme under the architecture of cell-free distributed massive multiple-input multiple-output (MIMO) systems. Cell-free distributed massive MIMO systems have powerful macro diversity gain and multi-user interference suppression capabilities to improve the performance of different slices, and are more flexible in terms of service types based on network slicing. In the proposed scheme, we utilize the long-term and short-term trends of network to achieve adaptive resource allocation at different timescales, so as to utilize resources more effectively and meet performance requirements in parallel. Moreover, multi-connectivity is utilized to improve link reliability and further improve system performance, and user clustering reduces the impact of pilot contamination on system performance. In order to implement the RAN control strategy effectively, an efficient two-level deep reinforcement learning framework is proposed and the multi-agent reinforcement learning algorithm is used to realize efficient network resource interaction in multi-device scenarios. Simulation results further verify the effectiveness of the proposed intelligent RAN slicing scheme.