Key technologies for wireless network digital twin towards smart railways

An emerging railway technology called smart railway promises to deliver higher transportation efficiency,enhanced comfort in services,and greater eco-friendliness.The smart railway is expected to integrate fifth-generation mobile communication(5G),Artificial Intelligence(AI),and other technologies,which poses new problems in the construction,operation and maintenance of railway wireless networks.Wireless Digital Twins(DTs),which have recently emerged as a new paradigm for the design of wireless networks,can address these problems and enable the whole lifecycle management of railway wireless networks.However,there are still many scientific issues and challenges for railway-oriented wireless DT.Relevant key technologies to solve these problems are introduced and described,including characterization of materials'physical-EM properties,autonomous reconstruction of Three-dimensional(3D)environment model,AI-empowered environmental cognition,Ray-Tracing(RT),model-based and AIbased RT acceleration,and generation of multi-spectra sensing data.Moreover,this paper presents our research results for each key technology and describes the wireless network planning and optimization system based on highperformance RT developed by our laboratory.This paper outlines the framework for realizing the wireless DT of smart railways,providing the direction for future research.

Radio Communication Scenarios in 5G-Railways

With the rapid development of railways,especially high-speed railways,there is an increasingly urgent demand for new wireless communication system for railways.Taking the mature 5G technology as an opportunity,5G-railways(5G-R)have been widely regarded as a solution to meet the diversified demands of railway wireless communications.For the design,deployment and improvement of 5GR networks,radio communication scenario classification plays an important role,affecting channel modeling and system performance evaluation.In this paper,a standardized radio communication scenario classification,including 18 scenarios,is proposed for 5GR.This paper analyzes the differences of 5G-R scenarios compared with the traditional cellular networks and GSM-railways,according to 5G-R requirements and the unique physical environment and propagation characteristics.The proposed standardized scenario classification helps deepen the research of 5G-R and promote the development and application of the existing advanced technologies in railways.

OTFS Modulation and PAPR Reduction for IoT-Railways

The internet of things(IoT)has been widely considered to be integrated with high-speed railways to improve safety and service.It is important to achieve reliable communication in IoT for railways(IoT-R)under high mobility scenarios and strict energy constraints.Orthogonal time frequency space(OTFS)modulation is a two-dimensional modulation technique that has the potential to overcome the challenges in high Doppler environments.In addition,OTFS can have lower peak-to-average power ratio(PAPR)compared to orthogonal frequency division multiplexing,which is especially important for the application of IoT-R.Therefore,OTFS modulation for IoT-R is investigated in this paper.In order to decrease PAPR of OTFS and promote the application of OTFS modulation in IoT-R,the peak windowing technique is used in this paper.This technique can reduce the PAPR of OTFS by reducing the peak power and does not require multiple iterations.The impacts of different window functions,window sizes and clipping levels on PAPR and bit error rate of OTFS are simulated and discussed.The simulation results show that the peak windowing technique can efficiently reduce the PAPR of OTFS for IoT-R.

Generative Adversarial Networks Based Digital Twin Channel Modeling for Intelligent Communication Networks

Integration of digital twin(DT)and wireless channel provides new solution of channel modeling and simulation,and can assist to design,optimize and evaluate intelligent wireless communication system and networks.With DT channel modeling,the generated channel data can be closer to realistic channel measurements without requiring a prior channel model,and amount of channel data can be significantly increased.Artificial intelligence(AI)based modeling approach shows outstanding performance to solve such problems.In this work,a channel modeling method based on generative adversarial networks is proposed for DT channel,which can generate identical statistical distribution with measured channel.Model validation is conducted by comparing DT channel characteristics with measurements,and results show that DT channel leads to fairly good agreement with measured channel.Finally,a link-layer simulation is implemented based on DT channel.It is found that the proposed DT channel model can be well used to conduct link-layer simulation and its performance is comparable to using measurement data.The observations and results can facilitate the development of DT channel modeling and provide new thoughts for DT channel applications,as well as improving the performance and reliability of intelligent communication networking.

Towards 6G: Paradigm of Realistic Terahertz Channel Modeling

Terahertz(THz)communications are envisioned as a key technology for the sixth-generation wireless communication system(6G).However,it is not practical to perform large-scale channel measurements with high degrees of freedom at THz frequency band.This makes empirical or stochastic modeling approaches relying on measurements no longer stand.In order to break through the bottleneck of scarce fulldimensional channel sounding measurements,this paper presents a novel paradigm for THz channel modeling towards 6G.With the core of high-performance ray tracing(RT),the presented paradigm requires merely quite limited channel sounding to calibrate the geometry and material electromagnetic(EM)properties of the three-dimensional(3D)environment model in the target scenarios.Then,through extensive RT simulations,the parameters extracted from RT simulations can be fed into either ray-based novel stochastic channel models or cluster-based standard channel model families.Verified by RT simulations,these models can generate realistic channels that are valuable for the design and evaluation of THz systems.Representing two ends of 6G THz use cases from microscopy to macroscopy,case studies are made for close-proximity communications,wireless connections on a desktop,and smart rail mobility,respectively.Last but not least,new concerns on channel modeling resulting from distinguishing features of THz wave are discussed regarding propagation,antenna array,and device aspects,respectively.

Vehicle-to-Infrastructure Channel Characterization in Urban Environment at 28 GHz

Both ultra-reliable low latency and high-data-rate communications are required by connective vehicles. Millimeter wave(mm Wave) with large bandwidth is a key technology to support high-data-rate communications. In this paper, the 28 GHz wideband vehicle-to-infrastructure channel is characterized for the urban environment in a major street in Manhattan. The deployment of the transmitter and the receiver, as well as the traffic models, are selected by considering the recommendation by 3GPP TR 37.885. Ray tracing simulator with calibrated electromagnetic parameters is employed in this work to practically conduct intensive simulations. The 3D environment model is reconstructed from OpenStreetMap. The power delay profile, path loss, root-meansquare delay spread, K-factor and so on, are extracted from the calibrated simulation results. The evolution of the parameters, as well as their statistical properties, are analyzed and modeled. The work of this paper helps the researchers understand the propagation channel for designing mmWave technologies and communication system in a similar scenario.

Wireless Relay Communication on High Speed Railway：Full Duplex or Half Duplex？

In this paper,we introduce one full-duplex(FD) relaying transmission scheme for high speed railway and compare its ergodic capacity and outage performance with half-duplex(HD) relaying scheme. Both decode-and-forward(DF) amplify-and-forward(AF) relay modes are considered. Moreover,the carriage penetration loss(CPL) and the self-interference(SI) cancellation ratio are investigated. We derive the closed-form expressions for the outage probability and ergodic capacity of both HD and FD relay transmission schemes. It is shown that when CPL is larger than a certain level,the FD relay can achieve better performance in terms of capacity and keep the outage probability in a low level. It is also found that three factors: AF or DF modes,CPL and SI cancellation ratio,can have impact on capacity performance,and that CPL is one decisive factor. Our results can provide theoretical supports for development and deployment of future wireless communication systems on high speed railways.

Multi-Scenario Millimeter Wave Wireless Channel Measurements and Sparsity Analysis

Wireless channel characteristics have significant impacts on channel modeling,estimation,and communication performance.While the channel sparsity is an important characteristic of wireless channels.Utilizing the sparse nature of wireless channels can reduce the complexity of channel modeling and estimation,and improve system design and performance analysis.Compared with the traditional sub6 GHz channel,millimeter wave(mmWave)channel has been considered to be more sparse in existing researches.However,most research only assume that the mmWave channel is sparse,without providing quantitative analysis and evaluation.Therefore,this paper evaluates the sparsity of mmWave channels based on mmWave channel measurements.A vector network analyzer(VNA)-based mmWave channel sounder is developed to measure the channel at 28 GHz,and multi-scenario channel measurements are conducted.The Gini index,Rician𝐾factor and rootmean-square(RMS)delay spread are used to measure channel sparsity.Then,the key factors affecting mmWave channel sparsity are explored.It is found that antenna steering direction and scattering environment will affect the sparsity of mmWave channel.In addition,the impact of channel sparsity on channel eigenvalue and capacity is evaluated and analyzed.

V2V Channel Characterization and Modeling for Underground Parking Garages

As an important part of the intelligent transportation system(ITS),vehicle-to-vehicle(V2V)communication will improve the safety and efficiency of the transportation system by realizing the information transmission between vehicles.The underground parking garage is a typical scenario in V2V communication,but as an indoor environment,it is different from the conventional outdoor road scenarios significantly.Therefore,the purpose of this paper is to analyze and model the V2V channel characteristics of an underground garage.In this paper,a novel channel measurement carried out in the underground garage is first introduced.Considering that there are a lot of obstacles in an underground garage,the non-line-of-sight(NLoS)condition is taken into account during the measurement.Then,channel characteristic parameters,including large-scale fading,delay spread,and K-factor,are analyzed based on the measured data.In addition,we also carry out channel angle of arrival analysis,show the distribution of azimuth of arrival(AOA)and elevation of arrival(EOA)of multipath components(MPC),and conduct a statistical analysis of the angular spread.In the process of analysis and modeling,we focus on the differences between line-of-sight(LoS)and NLoS conditions.The analysis and conclusions presented in this paper will enrich the understanding of V2V channel and benefit the design of V2V communications.

Contention-Based Nonorthogonal Massive Access with Massive MIMO

The sporadic communication character of massive machine-type communication systems provides natural advantages to utilize the principle of compressive sensing(CS).However,due to the high computational complexity of CS algorithms,CS-based contention-free access schemes have limited scalability and high computational complexity for massive access with user-specific pilots.To address these problems,in this paper,we propose a new contention-based scheme for CSbased massive access,which can support the sporadic access of massive devices(more than one million devices)with limited resources.Furthermore,an advanced receiver algorithm is designed to solve the optimal solutions for the proposed scheme,which utilizes various prior information to enhance the performance.In specific,the joint sparsity between the channel and data is used to improve the accuracy of pilot detection,and the information of modulation and cyclic redundancy check is exploited for channel correction to improve the performance of data recovery.The simulation results show that the proposed scheme can achieve improved active user detection performance and data recovery accuracy than existing methods.

Multi-Frequency Channel Characterization for Massive MIMO Communications in Lobby Environment

In this paper,a massive multiple input multiple output(MIMO)channel measurement campaign with two setups is conducted in an indoor lobby environment.In the first setup,two types of 256-element virtual uniform rectangular arrays(URAs),i.e.,the 4×64 virtual URA and the 64×4 virtual URA are used.The carrier frequency is 11 GHz;in the second setup,measurements are performed at 4,6,11,13,15,18 GHz at two different user locations.The channel characterization is presented by investigating the typical channel parameters,including average power delay profile(APDP),K factor,root mean square(RMS)delay spread,and coherence bandwidth.Moreover,the channel characteristics in angular domain are investigated by applying the space-alternating generalized expectation-maximization(SAGE)algorithm.The extracted multipath components(MPCs)are preliminarily clustered by visual inspection,and related to the interacting objects(IOs)in physical environment.Multipath structures at multiple frequency bands are examined.Direction spread of departure is estimated to evaluate the directional dispersion at the base station(BS)side.The results in this paper can help to reveal the propagation mechanisms in massive MIMO channels,and provide a foundation for the design and application of the practical massive MIMO system.

AI Assisted PHY in Future Wireless Systems: Recent Developments and Challenges

Nowadays,the rapid development of artificial intelligence(AI)provides a fresh perspective in designing future wireless communication systems.Innumerable attempts exploiting AI methods have been carried out,which results in the state-of-the-art performance in many different areas of wireless communications.In this article,we present the most recent and insightful developments that demonstrate the potentials of AI techniques in different physical layer(PHY)components and applications including channel characterization,channel coding,intelligent signal identification,channel estimation,new PHY for random access in massive machine-type communication(mMTC),massive multiple-input multiple-output(MIMO)power control and PHY resource management.Open challenges and potential future directions are identified and discussed along this research line.

Coalition Game Based Full-Duplex Concurrent Scheduling in Millimeter Wave Wireless Backhaul Network

With the development of self-interference(SI) cancelation technology, full-duplex(FD) communication becomes possible. FD communication can theoretically double the spectral efficiency. When the time slot(TS) resources are limited and the number of flows is large, the scheduling mechanism of the flows becomes more important. Therefore, the effectiveness of FD scheduling mechanism for the flows is studied in millimeter wave wireless backhaul network with the limited TS resources. We proposed a full duplex concurrent scheduling algorithm based on coalition game(FDCG) to maximize the number of flows with their QoS requirements satisfied. We transformed the problem of maximizing the number of flows with their QoS requirements satisfied into the problem of maximizing sum rate of concurrently scheduled flows in each slot. We obtained the scheduled flows with maximum sum rate in first slot by using coalition game.And then with certain restrictions, the maximum sum rate of concurrently scheduled flows can also be achieved in subsequent time slots. The simulation results show that the proposed FDCG algorithm canachieve superior performance in terms of the number of flows that meet their QoS requirements and system throughput compared with other three algorithms.

OTFSmodulation performance in a satellite-to-ground channel at sub-6-GHz and millimeter-wave bands with high mobility

Orthogonal time frequency space(OTFS)modulation has been widely considered for high-mobility scenarios.Satellite-to-ground communications have recently received much attention as a typical high-mobility scenario and face great challenges due to the high Doppler shift.To enable reliable communications and high spectral efficiency in satellite mobile communications,we evaluate OTFS modulation performance for geostationary Earth orbit and low Earth orbit satellite-to-ground channels at sub-6-GHz and millimeter-wave bands in both lineof-sight and non-line-of-sight cases.The minimum mean squared error with successive detection(MMSE-SD)is used to improve the bit error rate performance.The adaptability of OTFS and the signal detection technologies in satellite-to-ground channels are analyzed.Simulation results confirm the feasibility of applying OTFS modulation to satellite-to-ground communications with high mobility.Because full diversity in the delay-Doppler domain can be explored,different terminal movement velocities do not have a significant impact on the performance of OTFS modulation,and OTFS modulation can achieve better performance compared with classical orthogonal frequency division multiplexing in satellite-to-ground channels.It is found that MMSE-SD can improve the performance of OTFS modulation compared with an MMSE equalizer.

A Ray Tracing and Joint Spectrum Based Clustering and Tracking Algorithm for Internet of Intelligent Vehicles

Driven by the rapid growth in information services provided by the Internet and the appearance of new multimedia applications,millimeter wave is foreseen as a key enabler towards the Internet of intelligent vehicles(IoIV)for urban traffic safety enhancement.In this regard,cluster-based channel modeling has become an important research topic in the realm of emergency communications.To fully understand the cluster-based channel model,a series of vehicle-to-infrastructure(V2I)channel simulations at 22.6 GHz are conducted by a three-dimensional ray tracing(RT)simulator.The clustering and tracking algorithm is proposed and analyzed from three aspects by the obtained simulation results.The multiple signal classification estimation spectrum is applied to restrain the influence of antenna sidelobes and identify targets at first.Based on the fundamentals,the clusters can be identified and subsequently tracked using the proposed approach.The impacts of antenna sidelobes,angle resolution of beam rotation,and non-line-of-sight propagation path on the performance of clustering and tracking are evaluated.The multi-component-level RT results are adopted as comparison benchmarks,which reflect the ground truth.This work aims to provide a full picture of the clustering characteristics for designing and analyzing emergency communication systems.

Internet of things for high-speed railways Block

As a new generation of application infrastructure and strategic emerging technologies,the internet of things(IoT)is an inevitable trend to be integrated into the rapid development of high-speed railways(HSR).This article first introduces the concept and key technologies of the HSR IoT,and then analyzes the HSR’s requirements for the IoTs.Based on these concepts,a system architecture of the HSR IoT is proposed to expand the in-depth applications of IoT in various fields of the HSR industry.Finally,future development trends of the HSR IoT are discussed.This article is expected to provide some useful insights into the study and evolution of HSR IoT.