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.
The Belief Propagation (BP) is an inference algorithm used to estimate marginal probability distributions for any Markov Random Field (MRF). In the realm of Low-Density Parity-Check (LDPC) codes that can be represented by MRF called Tanner graphs, the BP is used as a decoding algorithm to estimate the states of bits sent through a noisy channel. Known to be optimal when the Tanner graph is a tree, the BP suffers from suboptimality when the Tanner graph has a loop-like topology. Furthermore, combinations of loops, namely the trapping sets, are particularly harmful for the decoding. To circumvent this problem were proposed other algorithms, like the Generalized Belief Propagation (GBP) that comes from statistical physics. This algorithm allows to absorb topological structures inside new nodes called regions. An advantage is that the resulting graph, the region graph, is not unique then according to its construction this region graph is a media for the GBP that can provide more accurate estimates than the BP. In this paper, we propose novel constructions of the region graph for the famous Tanner code of length N = 155 by making use of the trapping sets as basis for the regions.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.