RoF distributed antenna architecture- and reinforcement learning-empowered real-time EMI immunity for highly reliable...

Highly reliable wireless train-ground communication immune to the electromagnetic interferences (EMIs) is of critical importance for the security and efficiency of high-speed railways (HSRs). However, the rapid development of HSRs (>52,000 km all over the world) brings great challenges on the conventional EMIs mitigation strategies featuring non-real-time and passive. In this paper, the convergence of radio-over-fiber distributed antenna architecture (RoF-DAA) and reinforcement learning technologies is explored to empower a real-time, cognitive and efficient wireless communication solution for HSRs, with strong immunity to EMIs. A centralized communication system utilizes the RoF-DAA to connect the center station (CS) and distributed remote radio units (RRUs) along with the railway track-sides to collect electromagnetic signals from environments. Real-time recognition of EMIs and interactions between the CS and RRUs are enabled by the RoF link featuring broad bandwidth and low transmission loss. An intelligent proactive interference avoidance scheme is proposed to perform EMI-immunity wireless communication. Then an improved Win or learn Fast-Policy Hill Climbing (WoLF-PHC) multi-agent reinforcement learning algorithm is adopted to dynamically select and switch the operation frequency bands of RRUs in a self-adaptive mode, avoiding the frequency channel contaminated by the EMIs. In proof-of-concept experiments and simulations, EMIs towards a single RRU and multiple RRUs in the same cluster and towards two adjacent RRUs in distinct clusters are effectively avoided for the Global System for Mobile communications-Railway (GSM-R) system in HSRs. The proposed system has a superior performance in terms of circumventing either static or dynamic EMIs, serving as an improved cognitive radio scheme to ensuring high security and high efficiency railway communication.