Autonomous Robustness Control for Fog Reinforcement in Dynamic Wireless Networks

Abstract

The sixth-generation (6G) of wireless communications systems will significantly rely on fog/edge network architectures for service provisioning. To realize this vision, AI-based fog/edge enabled reinforcement solutions are needed to serve highly stringent applications using dynamically varying resources. In this paper, we propose a cognitive dynamic fog/edge network where primary nodes (PNs) temporarily share their resources and act as fog nodes (FNs) for secondary nodes (SNs). Under this architecture, that unleashes multiple access opportunities, we design distributed fog probing schemes for SNs to search for available connections to access neighbouring FNs. Since the availability of these connections varies in time, we develop strategies to enhance the robustness to the uncertain availability of channels and fog nodes, and reinforce the connections with the FNs. A robustness control optimization is formulated with the aim to maximize the expected total long-term reliability of SNs’ transmissions. The problem is solved by an online robustness control (ORC) algorithm that involves online fog probing and an index-based connectivity activation policy derived from restless multi-armed bandits (RMABs) model. Simulation results show that our ORC scheme significantly improves the network robustness, the connectivity reliability and the number of completed transmissions. In addition, by activating the connections with higher indexes, the total long-term reliability optimization problem is solved with low complexity.