Physical Layer Enhancement for Next-Generation Railway Communication Systems

Abstract

This paper presents an overview of the challenges and state-of-the-art physical layer enhancement designs for next-generation railway communication, also known as high-speed train (HST) communication. The physical layer design for the HST should adapt from its counterpart in the generalpurpose network because of the harsh propagation environment and extreme conditions, stringent latency and reliability requirements of dedicated railway applications, and frequency band scarcity caused by regulation. In this survey, we examine how conventional multiple-input-multiple-output (MIMO) family techniques such as beamforming, multi-cell MIMO, and relays can enhance the physical layer performance for HST. Physical layer enhancement assisted by novel reconfigurable intelligent surface (RIS) technology was also analyzed from different perspectives. Dedicated control channels, reference signals, waveforms, and numerology designs for train-to-infrastructure (T2I) and train-to-train (T2T) communication in sidelinks are also reviewed. Finally, a brief introduction to artificial intelligence (AI)/machine learning (ML)-aided HST physical layer design is provided. Several promising research avenues have also been suggested.

Optimized DM-RS Configuration for Improved 5G New Radio Network Capacity and Performance

“…A group of papers is dedicated to system design and in particular numerology, frame structure, reference signals, multi-antenna schemes, cell-search and random-access procedures to support high-speed mobility use cases, with a focus on high-speed trains [9][10][11]. Another group of papers [12][13][14] has a similar focus, with the only difference being that some non-terrestrial-network use cases are covered, along with V2X communication with a focus on the design of sidelink.…”

“…The channel time variations due to Doppler effects are closely related to the velocity ( ν) of the transmitter/receiver, where variation between OFDM symbols may become significant when speed is increased. The Doppler effect is mostly modeled by the Jakes model [11,21], the autocorrelation function of which is the first-order Bessel function J 0 (2πν max ∆t) where ν max is the maximum Doppler shift (calculated as ν max = Fc * v/c), Fc is the carrier frequency, v is the velocity and ∆t is the time separation. An autocorrelation channel function above 67% is considered good enough to secure smooth communication (marked in Figures 4 and 5 with a red dotted line).…”

Short-Term Arrival Delay Time Prediction in Freight Rail Operations Using Data-Driven Models

“…This includes ensuring that the infrastructure is well-maintained, properly managed, and capable of meeting the current transportation system's demands. Furthermore, using technology and data analytics to optimize rail infrastructure and improve overall performance of the rail transport sector is critical [2], [3].…”