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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.

In this paper, we focus on the behavior of the Belief Propagation (BP) algorithm when decoding the Low-Density Parity-Check (LDPC) code of rate 3/4 in the DVB-S2 standard. By studying the topological structure of its Tanner graph, we raise properties inherent to the degree distribution that turns out to be strongly correlated with the decoding failures. The irregularity of the degrees seriously damages the performance, visible by an abrupt error floor. We accordingly propose a novel model of the error events based on the degree distribution which helps simulate typical error events and observing the flaws of the DVB-S2 code. This work could be used as a good basis to design future codes with a better decoding behavior, especially in the error floor region.