A Framework for High-Speed Passenger Train Wireless Network Radio Evaluations

By 2030, the United States Federal Transit Administration (FTA) plans to have High Speed Train (HST) systems deployed that span over 12,000 miles across the US. Given the rapidly accelerating growth in consumers demand for fast on-board Internet services, there is a need for a robust and dedicated railroad wireless network architecture for their onboard and Train-to-Ground (T2G) communication systems. And while there are several potential candidates for radio access technologies (RAT), a full understanding of the benefits and drawbacks of each is still missing. We therefore have developed and studied a simulation framework that offers railroads the ability to perform an in-depth evaluation of capabilities for different RATs in terms of interoperability, throughput, handover and bit error rate for various user-driven scenarios. The framework is capable of studying and analyzing conditions such as network performance at different train velocities, base station spacing requirements, as well as analyzing US-specific geographical or track-related architectural scenarios. Our Past experiences in researching railroad wireless solutions have shown that wireless network performance varies widely in environments like tunnels, viaducts, bridges, stations, etc. The simulator offers the network designers significant flexibility in terms of defining parameters to create simulation scenarios and obtaining a detailed understanding of network performance. The work has created a novel, flexible and adaptable simulation framework for high-speed passenger train wireless network evaluation. The simulation tool supports 220MHz-100GHz systems for simulating LTE and 5G-New Radio (5G-NR), and it can support other technologies such as 220MHz PTC, in a time-variant channel. In this paper we present the architecture and the capabilities of the simulator with a sample scenario evaluation. The developed framework aims to support HST wireless communication designers to conduct more detailed analyses and to make more informed decisions in optimizing system deployments.