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

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

A General 3D Non-Stationary 5G Wireless Channel Model

A novel unified framework of geometry-based stochastic models (GBSMs) for the fifth generation (5G) wireless communication systems is proposed in this paper. The proposed general 5G channel model aims at capturing small-scale fading channel characteristics of key 5G communication scenarios, such as massive multiple-input multiple-output (MIMO), high-speed train (HST), vehicle-to-vehicle (V2V), and millimeter wave (mmWave) communication scenarios. It is a three-dimensional (3D) non-stationary channel model based on the WINNER II and Saleh-Valenzuela (SV) channel models considering array-time cluster evolution. Moreover, it can easily be reduced to various simplified channel models by properly adjusting model parameters. Statistical properties of the proposed general 5G small-scale fading channel model are investigated to demonstrate its capability of capturing channel characteristics of various scenarios, with excellent fitting to some corresponding channel measurements

Massive MIMO channel modelling for 5G wireless communication systems

Massive Multiple-Input Multiple-Output (MIMO) wireless communication systems, equipped with tens or even hundreds of antennas, emerge as a promising technology for the Fifth Generation (5G) wireless communication networks. To design and evaluate the performance of massive MIMO wireless communication systems, it is essential to develop accurate, flexible, and efficient channel models which fully reflect the characteristics of massive MIMO channels. In this thesis, four massive MIMO channel models have been proposed. First, a novel non-stationary wideband multi-confocal ellipse Two-Dimensional (2-D) Geometry Based Stochastic Model (GBSM) for massive MIMO channels is proposed. Spherical wavefront is assumed in the proposed channel model, instead of the plane wavefront assumption used in conventional MIMO channel models. In addition, the Birth-Death (BD) process is incorporated into the proposed model to capture the dynamic properties of clusters on both the array and time axes. Second, we propose a novel theoretical non-stationary Three-Dimensional (3-D) wideband twin-cluster channel model for massive MIMO communication systems with carrier frequencies in the order of gigahertz (GHz). As the dimension of antenna arrays cannot be ignored for massive MIMO, nearfield effects instead of farfield effects are considered in the proposed model. These include the spherical wavefront assumption and a BD process to model non-stationary properties of clusters such as cluster appearance and disappearance on both the array and time axes. Third, a novel Kronecker Based Stochastic Model (KBSM) for massive MIMO channels is proposed. The proposed KBSM can not only capture antenna correlations but also the evolution of scatterer sets on the array axis. In addition, upper and lower bounds of KBSM channel capacities in both the high and low Signal-to-Noise Ratio (SNR) regimes are derived when the numbers of transmit and receive antennas are increasing unboundedly with a constant ratio. Finally, a novel unified framework of GBSMs for 5G wireless channels is proposed. The proposed 5G channel model framework aims at capturing key channel characteristics of certain 5G communication scenarios, such as massive MIMO systems, High Speed Train (HST) communications, Machine-to-Machine (M2M) communications, and Milli-meter Wave (mmWave) communications

How to Extend 3D GBSM Model to RIS Cascade Channel with Non-ideal Phase Modulation?

Reconfigurable intelligent surface (RIS) is seen as a promising technology for next-generation wireless communications, and channel modeling is the key to RIS research. However, traditional model frameworks only support Tx-Rx channel modeling. In this letter, a RIS cascade channel modeling method based on a geometry-based stochastic model (GBSM) is proposed, which follows a 3GPP standardized modeling framework. The main improvements come from two aspects. One is to consider the non-ideal phase modulation of the RIS element, so as to accurately include its phase modulation characteristic. The other is the Tx-RIS-Rx cascade channel generation method based on the RIS radiation pattern. Thus, the conventional Tx-Rx channel model is easily expanded to RIS propagation environments. The differences between the proposed cascade channel model and the channel model with ideal phase modulation are investigated. The simulation results show that the proposed model can better reflect the dependence of RIS on angle and polarization.Comment: 5 pages, 5 figure

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

Accuracy-Complexity Tradeoff Analysis and Complexity Reduction Methods for Non-Stationary IMT-A MIMO Channel Models

open access journalHigh-mobility wireless communication systems have attracted growing interests in recent years. For the deployment of these systems, one fundamental work is to build accurate and efficient channel models. In high-mobility scenarios, it has been shown that the standardized channel models, e.g., IMT-Advanced (IMT-A) multiple-input multiple-output (MIMO) channel model, provide noticeable longer stationary intervals than measured results and the wide-sense stationary (WSS) assumption may be violated. Thus, the non-stationarity should be introduced to the IMT-A MIMO channel model to mimic the channel characteristics more accurately without losing too much efficiency. In this paper, we analyze and compare the computational complexity of the original WSS and non-stationary IMT-A MIMO channel models. Both the number of real operations and simulation time are used as complexity metrics. Since introducing the nonstationarity to the IMT-A MIMO channel model causes extra computational complexity, some computation reduction methods are proposed to simplify the non-stationary IMT-A MIMO channel model while retaining an acceptable accuracy. Statistical properties including the temporal autocorrelation function, spatial cross-correlation function, and stationary interval are chosen as the accuracy metrics for verifications. It is shown that the tradeoff between the computational complexity and modeling accuracy can be achieved by using these proposed complexity reduction methods

Massive MIMO Channel Models: A Survey

The exponential traffic growth of wireless communication networks gives rise to both the insufficient network capacity and excessive carbon emissions. Massive multiple-input multiple-output (MIMO) can improve the spectrum efficiency (SE) together with the energy efficiency (EE) and has been regarded as a promising technique for the next generation wireless communication networks. Channel model reflects the propagation characteristics of signals in radio environments and is very essential for evaluating the performances of wireless communication systems. The purpose of this paper is to investigate the state of the art in channel models of massive MIMO. First, the antenna array configurations are presented and classified, which directly affect the channel models and system performance. Then, measurement results are given in order to reflect the main properties of massive MIMO channels. Based on these properties, the channel models of massive MIMO are studied with different antenna array configurations, which can be used for both theoretical analysis and practical evaluation

Massive MIMO Channel Characterization and Modeling: The Present and the Future

One of the technologies aimed to provide large increase in data rate, enhanced spectral efficiency, transmit power efficiency, high sum rates, and increase link reliability for the fifth generation network (5G) is the massive multiple input multiple output (MIMO) antenna system. The projected benefits of massive MIMO depend on the propagation environment. However, due to the non wide-sense stationarity properties of massive MIMO, small scale characterization (SSC) is not enough for modeling its propagation channel as the spatial domain is also required. Giving consideration to the dynamic adaptation of the elevation angles which is not captured in 2D channel models will open up new possibilities for 3D beamforming which will introduce considerable performance gains for 5G network capacity enhancement. In this paper therefore, we review the various non wide-sense stationary channel parameters for characterizing massive MIMO channel particularly in the 3D plane and their methods of measurement, All through the discussion, we identified outstanding research challenges in these areas and their future directions