A generic non-stationary MIMO channel model for different high-speed train scenarios

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper proposes a generic non-stationary wideband geometry-based stochastic model (GBSM) for multiple-input multiple-output (MIMO) high-speed train (HST) channels. The proposed generic model can be applied on the three most common HST scenarios, i.e., open space, viaduct, and cutting scenarios. A good agreement between the statistical properties of the proposed generic model and those of relevant measurement data from the aforementioned scenarios demonstrates the utility of the proposed channel model

Propagation channel characterisation and modelling for high-speed train communication systems

High-mobility scenarios, e.g., High-Speed Train (HST) scenarios, are expected to be typical scenarios for the Fifth Generation (5G) communication systems. With the rapid development of HSTs, an increasing volume of wireless communication data is required to be transferred to train passengers. HST users demand high network capacity and reliable communication services regardless of their locations or speeds, which are beyond the capability of current HST communication systems. The features of HST channels are significantly different from those of low-mobility cellular communication systems. For a proper design and evaluation of future HST wireless communication systems, we need accurate channel models that can mimic the underlying channel characteristics, especially the non-stationarity for different HST scenarios. Inspired by the lack of such accurate HST channel models in the literature, this PhD project is devoted to the modelling and simulation of non-stationary Multiple-Input Multiple-Output (MIMO) channels for HST communication systems. In this thesis, we first give a comprehensive review of the measurement campaigns conducted in different HST scenarios and address the recent advances in HST channel models. We also highlight the key challenges of HST channel measurements and models. Then, we study the characterisation of non-stationary channels and propose a theoretical framework for deriving the statistical properties of these channels. HST wireless communication systems encounter different channel conditions due to the difference of surrounding geographical environments or scenarios. HST channel models in the literature have either considered large-scale parameters only and/or neglected the non-stationarity of HST channels and/or only consider one of the HST scenarios. Therefore, we propose a novel generic non-stationary Geometry-Based Stochastic Model (GBSM) for wideband MIMO HST channels in different HST scenarios, i.e., open space, viaduct, and cutting. The corresponding simulation model is then developed with angular parameters calculated by the Modified Method of Equal Area (MMEA). The system functions and statistical properties of the proposed channel models are thoroughly studied. The proposed generic non-stationary HST channel models are verified by measurements in terms of stationary time for the open space scenario and the Autocorrelation Function (ACF), Level Crossing Rate (LCR), and stationary distance for the viaduct and cutting scenarios. Transmission techniques which are capable of utilising Three-Dimensional (3D) spatial dimensions are significant for the development of future communication systems. Consequently, 3D MIMO channel models are critical for the development and evaluation of these techniques. Therefore, we propose a novel 3D generic non-stationary GBSM for wideband MIMO HST channels in the most common HST scenarios. The corresponding simulation model is then developed with angular parameters calculated by the Method of Equal Volume (MEV). The proposed models considers several timevarying channel parameters, such as the angular parameters, the number of taps, the Ricean K-factor, and the actual distance between the Transmitter (Tx) and Receiver (Rx). Based on the proposed generic models, we investigate the impact of the elevation angle on some of the channel statistical properties. The proposed 3D generic models are verified using relevant measurement data. Most standard channel models in the literature, like Universal Mobile Telecommunications System (UMTS), COST 2100, and IMT-2000 failed to introduce any of the HST scenarios. Even for the standard channel models which introduced a HST scenario, like IMT-Advanced (IMT-A) and WINNER II channel models, they offer stationary intervals that are noticeably longer than those in measured HST channels. This has inspired us to propose a non-stationary IMT-A channel model with time-varying parameters including the number of clusters, powers, delays of the clusters, and angular parameters. Based on the proposed non-stationary IMT-A channel model, important statistical properties, i.e., the time-variant spatial Cross-correlation Function (CCF) and time-variant ACF, are derived and analysed. Simulation results demonstrate that the stationary interval of the developed non-stationary IMT-A channel model can match that of relevant HST measurement data. In summary, the proposed theoretical and simulation models are indispensable for the design, testing, and performance evaluation of 5G high-mobility wireless communication systems in general and HST ones in specific

MASSIVE MIMO FOR HIGH-SPEED TRAIN COMMUNICATION SYSTEMS

With the current development in wireless communications in high-mobility systems such as high-speed train (HST), the HST scenario is accepted as among the different scenarios for the fifth-generation (5G). Massive Multiple-Input-Multiple-Output (MIMO) systems, which are equipped with tens or hundreds of antennas has become an improved MIMO system which can assist in achieving the ever-growing demand of data for 5G wireless communication systems. In this study, the associated 5G technologies, as well as the equivalent channel modeling in HST settings and the challenges of deploying massive MIMO on HST, was investigated The channel model was modeled using the WINNER II channel model. With regrads, the proposed non-stationary IMT-A massive MIMO channel models, the essential statistical properties such as the spatial cross-correlation function (CCF), local temporal autocorrelation function (ACF) of the massive MIMO channel model using different propagation scenarios such as open space, viaduct and cutting was analyzed and investigated. The results from the simulations were compared with the analytical results in other to show that the statistical properties vary with time as a result of the non-stationarity of the proposed channel model. The agreement between the stationary interval of the non-stationary IMT-A channel model and the HST under different propagation scenarios shows the efficiency of the proposed channel model. Based on findings; the impact of the deployment of a large antenna on the channel capacity should be thoroughly investigated under different HST propagation scenario. Also, more HST train propagation scenarios such as the tunnel, hilly terrain, and the station should be considered in the non-stationary IMT-A massive MIMO channel models

A Non-Stationary IMT-Advanced MIMO Channel Model for High-Mobility Wireless Communication Systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.With the recent developments of high-mobility wireless communication systems, e.g., high-speed train (HST) and vehicle-to-vehicle (V2V) communication systems, the ability of conventional stationary channel models to mimic the underlying channel characteristics has widely been challenged. Measurements have demonstrated that the current standardized channel models, like IMT-Advanced (IMT-A) and WINNER II channel models, offer stationary intervals that are noticeably longer than those in measured HST channels. In this paper, we propose a non-stationary channel model with time-varying parameters including the number of clusters, the powers and the delays of the clusters, the angles of departure (AoDs), and the angles of arrival (AoAs). Based on the proposed non-stationary IMT-A channel model, important statistical properties, i.e., the local spatial cross-correlation function (CCF) and local temporal autocorrelation function (ACF) are derived and analyzed. Simulation results demonstrate that the statistical properties vary with time due to the non-stationarity of the proposed channel model. An excellent agreement is achieved between the stationary interval of the developed non-stationary IMT-A channel model and that of relevant HST measurement data, demonstrating the utility of the proposed channel model

Terahertz Wireless Channels: A Holistic Survey on Measurement, Modeling, and Analysis

Terahertz (0.1-10 THz) communications are envisioned as a key technology for sixth generation (6G) wireless systems. The study of underlying THz wireless propagation channels provides the foundations for the development of reliable THz communication systems and their applications. This article provides a comprehensive overview of the study of THz wireless channels. First, the three most popular THz channel measurement methodologies, namely, frequency-domain channel measurement based on a vector network analyzer (VNA), time-domain channel measurement based on sliding correlation, and time-domain channel measurement based on THz pulses from time-domain spectroscopy (THz-TDS), are introduced and compared. Current channel measurement systems and measurement campaigns are reviewed. Then, existing channel modeling methodologies are categorized into deterministic, stochastic, and hybrid approaches. State-of-the-art THz channel models are analyzed, and the channel simulators that are based on them are introduced. Next, an in-depth review of channel characteristics in the THz band is presented. Finally, open problems and future research directions for research studies on THz wireless channels for 6G are elaborated.Comment: to appear in IEEE Communications Surveys and Tutorial

Statistical Properties of High-Speed Train Wireless Channels in Different Scenarios

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In this paper, we compare the statistical properties of high-speed train (HST) wireless channels in different scenarios using a generic non-stationary HST channel model that has been verified by channel measurements (Ghazal et al., 2015). We mainly focus our comparison and analysis on the three most common HST scenarios, i.e., the rural area, cutting, and viaduct scenarios. Several channel statistical properties such as the temporal autocorrelation function (ACF), space cross-correlation function (CCF), and space- Doppler (SD) power spectrum density (PSD) are investigated. The impacts of different scenario- specific parameters on the channel statistical properties are also studied via numerical analysis

Realistic geometry-based stochastic channel models for advanced wireless MIMO systems

The employment of multiple antennas at both the Transmitter (Tx) and Receiver (Rx) enables the so-called Multiple-Input Multiple-Output (MIMO) technologies to greatly improve the link reliability and increase the overall system capacity. MIMO has been recommended to be employed in various advanced wireless communication systems, e.g., the Fourth Generation (4G) wireless systems and beyond. For the successful design, performance test, and simulation of MIMO wireless communication systems, a thorough understanding of the underlying MIMO channels and corresponding models are indispensable. The approach of geometry-based stochastic modelling has widely been used due to its advantages, such as convenience for theoretical analysis and mathematical tractability. In addition, wireless Vehicle-to-Vehicle (V2V) communications play an important role in mobile relay-based cellular networks, vehicular ad hoc networks, and intelligent transportation systems. In V2V communication systems, both the Tx and Rx are in motion and equipped with low elevation antennas. This is di erent from conventional Fixed-to-Mobile (F2M) cellular systems, where only one terminal moves. This PhD project is therefore devoted to the modelling and simulation of wireless MIMO channels for both V2V and F2M communication systems. In this thesis, we rst propose a novel narrowband Three Dimensional (3D) theoretical Regular-Shape Geometry Based Stochastic Model (RS-GBSM) and the corresponding Sum-of-Sinusoids (SoS) simulation model for non-isotropic MIMO V2V Ricean fading channels. The proposed RS-GBSM has the ability to study the impact of the Vehicular Tra c Density (VTD) on channel statistics and jointly considers the azimuth and elevation angles by using the von Mises-Fisher (VMF) distribution. Moreover, a novel parameter computation method is proposed for jointly calculating the azimuth and elevation angles in the SoS channel simulator. Based on the proposed 3D theoretical RS-GBSM and its SoS simulation model, statistical properties are derived and thoroughly investigated. The impact of the elevation angle in the 3D model on key statistical properties is investigated by comparing with those of the corresponding Two Dimensional (2D) model. It is demonstrated that the 3D model is more practical to characterise real V2V channels, in particular for pico-cell scenarios. Secondly, actual V2V channel measurements have shown that the modelling assumption of Wide Sense Stationary (WSS) is valid only for very short time intervals. This fact inspires the requirement of non-WSS V2V channel models. Therefore, we propose a novel 3D theoretical wideband MIMO non-WSS V2V RS-GBSM and corresponding SoS simulation model. Due to the dynamic movement of both the Tx and Rx, the Angle of Departure (AoD) and Angle of Arrival (AoA) are time-variant, which makes our model non-stationary. The proposed RS-GBSMs are su ciently generic and adaptable to mimic various V2V scenarios. Furthermore, important local channel statistical properties are derived and thoroughly investigated. The impact of non-stationarity on these channel statistical properties is investigated by comparing with those of the corresponding WSS model. The proposed non-WSS RS-GBSMs are validated by measurements in terms of the channel stationary time. Thirdly, realistic MIMO channel models with a proper trade-o between accuracy and complexity are indispensable for the practical application. By comparing the accuracy and complexity of two latest F2M standardised channel models (i.e., LTE-A and IMT-A channel models), we employ some channel statistical properties as the accuracy metrics and the number of Real Operations (ROs) as the complexity metric. It is shown that the LTE-A MIMO channel model is simple but has signi cant aws in terms of the accuracy. The IMT-A channel model is complicated but has better accuracy. Therefore, we focus on investigating various complexity reduction methods to simplify the IMT-A channel model. The results have shown that the proposed methods do not degrade much the accuracy of the IMT-A channel model, whereas they can signi cantly reduce the complexity in terms of the number of ROs and channel coe cients computing time. Finally, to investigate the non-stationarity of the IMT-A MIMO channel model, we further propose a non-WSS channel model with time-varying AoDs and AoAs. The proposed time-varying functions can be applied to various scenarios according to moving features of Moving Clusters (MCs) and a Mobile Station (MS). Moreover, the impacts of time-varying AoDs and AoAs on local statistical properties are investigated thoroughly. Simulation results prove that statistical properties are varied with time due to the non-stationarity of the proposed channel model. In summary, the proposed reference models and channel simulators are useful for the design, testing, and performance evaluation of advanced wireless V2V and F2M MIMO communication systems

Model-Based Deep Learning

Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physics, prior information and additional domain knowledge. Simple classical models are useful but sensitive to inaccuracies and may lead to poor performance when real systems display complex or dynamic behavior. On the other hand, purely data-driven approaches that are model-agnostic are becoming increasingly popular as datasets become abundant and the power of modern deep learning pipelines increases. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance, especially for supervised problems. However, DNNs typically require massive amounts of data and immense computational resources, limiting their applicability for some signal processing scenarios. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches. Such model-based deep learning methods exploit both partial domain knowledge, via mathematical structures designed for specific problems, as well as learning from limited data. In this article we survey the leading approaches for studying and designing model-based deep learning systems. We divide hybrid model-based/data-driven systems into categories based on their inference mechanism. We provide a comprehensive review of the leading approaches for combining model-based algorithms with deep learning in a systematic manner, along with concrete guidelines and detailed signal processing oriented examples from recent literature. Our aim is to facilitate the design and study of future systems on the intersection of signal processing and machine learning that incorporate the advantages of both domains