Channel Model Framework Research Articles

Characterization of Low-Latency Next-Generation eVTOL Communications: From Channel Modeling to Performance Evaluation

Next-generation wireless communication networks are expected to offer extremely high data rates supported by very low latency and radically new applications, which require a new wireless radio technology paradigm. However, it is crucial to assist the radio link over the fast varying and highly dynamic channel to satisfy the diverse requirements of next-generation wireless networks. Recently, communication via autonomous electric vertical takeoff and landing (eVTOL) has gained momentum, owing to its potential for cost-effective network deployment. It is considered one of the most promising technologies conceived to support smart radio terminals. However, to provide efficient and reliable communications between ground base stations and eVTOLs as well as between eVTOLs and other eVTOLs, realistic eVTOL channel models are indispensable. In this paper, we propose a nonstationary geometry-based stochastic channel model for eVTOL communication links. The proposed eVTOL channel model framework considers time-domain nonstationarity and arbitrary eVTOL trajectory and is sufficiently general to support versatile C bands. One of the critical challenges for eVTOL is the fast vertical takeoff and landing flight patterns affecting the regular propagation communication channel. Moreover, we present a new method for estimating the SNR over the non-stationary fast dynamic time-variant eVTOL channel by utilizing the sliding window adaptive filtering technique. Furthermore, we present an information–theoretic approach to characterize the end-to-end transmission delay over the eVTOL channel and prove that the optimal transmission scheme strongly depends upon the eVTOL link configuration. In addition, to analyze the occurrence of deep fade regions in eVTOL links, we analyze the outage probability, which is an important performance metric for wireless channels operating over dynamic fading channels, and make an important observation that the outage probability increases non-linearly with the eVTOL height. Furthermore, we consider the commercially available eVTOL specifications and data to validate the channel model and analyze the Doppler shift and latency for the exponential acceleration and exponential deceleration velocities profiles during the takeoff and landing operation. This paper provides a new and practical approach for the design, optimization, and performance evaluation of future eVTOL-assisted next-generation wireless communications.

Channel Distribution Learning: Model-Driven GAN-Based Channel Modeling for IRS-Aided Wireless Communication

Intelligent reflecting surface (IRS) is a promising new technology that is able to create a favorable wireless signal propagation environment by collaboratively reconfiguring the passive reflecting elements yet with low hardware and energy cost. In IRS-aided wireless communication systems, channel modeling is a fundamental task for communication algorithm design and performance optimization, which however is also very challenging since in-depth domain knowledge and technical expertise in radio signal propagations are required, especially for modeling the high-dimensional cascaded base station (BS)-IRS and IRS-user channels (also referred to as the reflected channels). In this paper, we propose a model-driven generative adversarial network (GAN)-based channel modeling framework to autonomously learn the reflected channel distribution, without complex theoretical analysis or data processing. The designed GAN (also named as IRS-GAN) is trained to reach the Nash equilibrium of a minimax game between a generative model and a discriminative model. For the single-user case, we propose to incorporate the special structure of the reflected channels into the design of the generative model. While for the multiuser case, we extend the IRS-GAN and present a multiuser IRS-GAN (abbreviated as IRS-GAN-M), where the distributions of the reflected channels associated with different users are learned simultaneously with reduced number of network parameters (as compared to the naive scheme that assigns a dedicated IRS-GAN for each user). Moreover, theoretical analysis is presented to prove that the minimax game in the IRS-GAN-M framework has a global optimum if the generative and discriminative models are given with enough capacity. Simulation results are presented to validate the effectiveness of the proposed IRS-GAN framework.

An ANN‐based channel modeling in 5G millimeter wave for a high‐voltage substation

In this work, an artificial neural network (ANN) based time-varying channel modeling framework is proposed, including a playback model and a prediction model. The purpose of the ANN-based modeling framework is to playback 5G measured radio channels at certain measurement positions, and further predict large scale channel parameters (LSCPs) at unmeasured positions with limited amount of measurement data. 28 GHz channel measurements were also conducted at a high-voltage substation for the first time worldwide to meet with 5G radio system deployment for China Energy Internet. Meanwhile, the performance of the playback channels is evaluated by comparison with the measurements and traditional geometry based stochastic modeling (GBSM) simulated channels. An optimized radial basis function (ORBF) ANN is applied in the prediction model, and the predicted LSCPs are compared with the other approaches, which shows that the ORBF has the best performance. This work offers a solution to predict radio channels and parameters in case of big measured or simulated channel datasets.

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

In this paper, a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G) systems is proposed. The proposed B5G channel model (B5GCM) is designed to capture various channel characteristics in (B)5G systems such as space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high delay resolution, time-variant velocities and directions of motion of the transmitter, receiver, and scatterers, spatial consistency, etc. By combining different channel properties into a general channel model framework, the proposed B5GCM is able to be applied to multiple frequency bands and multiple scenarios, including massive multiple-input multiple-output (MIMO), vehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz (mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are obtained and compared with those of standard 5G channel models and corresponding measurement data, showing the generalization and usefulness of the proposed model.

Channel Measurement and Modeling Prototype for IEEE 802.22-Based Regional Area Networks

During the design and development of a reliable transceiver, its performance and robustness are evaluated using channel models that simulate the propagation channel characteristics in a real-world environment. In the case of wide-area communication systems in rural areas such as those based on TV white spaces, long delay multipath fading is one of the dominant factors impairing communication quality, and it is important to apply realistic multipath fading channel models in various application scenarios. Therefore, it is necessary to develop channel measurement and modeling systems that adaptively obtain channel factors and reflect multipath propagation characteristics in various wide-area communication environments. In this study, we developed a flexible channel measurement and modeling prototype applicable to mobile IEEE 802.22-based wide-area communication systems, which can be simply implemented by using programmable hardware and software. The prototype includes a new channel modeling framework that applies an improved double exponential slope model to extract the essential factors of multipath propagation characteristics. To validate the developed prototype, a fading emulator is applied to it to measure the raw data used for modeling. As a result, the multipath fading characteristics measured with the prototype were successfully modeled and reproduced by the proposed channel modeling framework with a high fitness of root mean square delay spread distribution. The derived bit error rate characteristics agreed better with the simulation results compared to the conventional channel model.

6G Wireless Channel Measurements and Models: Trends and Challenges

In this article, we present our vision of the application scenarios, performance metrics, and potential key technologies of 6G wireless communication networks. We then comprehensively survey 6G wireless channel measurements, characteristics, and models for all frequency bands and all scenarios, focusing on millimeter-wave (mm-wave), terahertz, and optical wireless communication channels under all spectra; satellite, unmanned aerial vehicle (UAV), maritime, and underwater acoustic communication channels under global coverage scenarios; and high-speed train (HST), vehicle-to-vehicle (V2V), ultra-massive multiple-input, multiple-output (MIMO), orbital angular momentum (OAM), and industry Internet of Things (IoT) communication channels under full application scenarios. We also provide future research challenges of 6G channel measurements, a general standard 6G channel model framework, and models for intelligent reflection surface (IRS)-based 6G technologies and artificial intelligence (AI)-enabled channel measurements and models.

A Novel Nonstationary 6G UAV-to-Ground Wireless Channel Model With 3-D Arbitrary Trajectory Changes

In order to provide reliable and efficient connections between unmanned aerial vehicles (UAVs) and ground stations (GSs), realistic UAV-to-ground channel models are indispensable. In this article, we propose a novel 3-D nonstationary geometry-based stochastic model (GBSM) for UAV-to-ground multiple-input-multiple-output (MIMO) channels. Distinctive UAV-to-ground channel characteristics, such as time-domain nonstationarity, distinctions between different altitudes, spatial consistency, and 3-D arbitrary UAV movement trajectories, are taken into account. By adjusting parameter settings, the proposed channel model framework is sufficiently general to support multiple frequency bands and multiple scenarios, including millimeter wave (mmWave) and massive MIMO configurations. Statistical properties, including power delay profile (PDP), stationary interval, space-time correlation function (STCF), and root-mean-square (RMS) delay spread are derived and analyzed for different frequencies and scenarios. The accuracy of the proposed model is validated by comparing its statistical properties with corresponding available channel measurements. The proposed channel model will provide a fundamental support for the design, performance evaluation, and optimization of future UAV integrated sixth-generation (6G) wireless networks.

A Big Data Enabled Channel Model for 5G Wireless Communication Systems

The standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling.

A 2-D Geometry-Based Stochastic Channel Model for 5G Massive MIMO Communications in Real Propagation Environments

Spherical wavefront and array nonstationarity due to physically large antenna array are two typical characteristics specific to massive multiple-input–multiple-output (M-MIMO) propagation. Unfortunately, traditional MIMO channel models need to be enhanced to better characterize these two new propagation features. In light of this limitation, this article proposes an enhanced two-dimensional geometry-based stochastic channel model for 5G M-MIMO communications in real propagation environments. Specifically, a coordinate-based channel model framework is first introduced. Based on this, the impact of spherical wavefront can be mathematically described with the aid of some spatial geometric manipulations. Moreover, a general Markov process is developed to model the array nonstationarity characteristic due to cluster appearance and disappearance along the array. Meanwhile, the associated approach to derive the state transition probabilities of the proposed Markov process is detailed. Finally, capabilities of the proposed channel model in characterizing spherical wavefront and array nonstationarity of M-MIMO channels are shown by the numerical simulations. Moreover, validity of our proposal is verified by the measurement data.

Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities

In modern wireless communication systems, wireless channel modeling has always been a fundamental task in system design and performance optimization. Traditional channel modeling methods, such as ray-tracing and geometry- based stochastic channel models, require in-depth domain-specific knowledge and technical expertise in radio signal propagations across electromagnetic fields. To avoid these difficulties and complexities, a novel generative adversarial network (GAN) framework is proposed for the first time to address the problem of autonomous wireless channel modeling without complex theoretical analysis or data processing. Specifically, the GAN is trained by raw measurement data to reach the Nash equilibrium of a MinMax game between a channel data generator and a channel data discriminator. Once this process converges, the resulting channel data generator is extracted as the target channel model for a specific application scenario. To demonstrate, the distribution of a typical additive white Gaussian noise channel is successfully approximated by using the proposed GAN-based channel modeling framework, thus verifying its good performance and effectiveness.

Virtual Drive Testing of Adaptive Antenna Systems in Dynamic Propagation Scenarios for Vehicle Communications

Virtual drive testing (VDT) has gained great interest from both industry and academia, owing to its promise to replay field trials in a controllable laboratory condition. VDT is especially appealing for vehicle communication scenarios, where actual field trials can be difficult to carry out in practice. Research work on VDT of adaptive antenna systems in dynamic propagation scenarios has been limited, mainly due to high VDT setup cost and lack of efficient dynamic propagation channel models. In this paper, we propose to jointly emulate adaptive antennas (i.e. beamformers) and radio channels in the radio channel emulator to reduce the VDT setup cost. A simple dynamic propagation channel framework, which is based on linear interpolation of propagation parameters of the stationary channel models, is also presented. We further experimentally evaluate the beamformer capability (i.e. beam tracking and null steering) in dynamic line-of-sight (LOS) and non-LOS scenarios in the proposed cost-effective conducted VDT setups. The simulation and measurement results have demonstrated the effectiveness of the beam tracking and nulling steering algorithms in dynamic propagation conditions in the presence of interfering signal. The proposed setup and dynamic channel modeling framework are valuable for the VDT of adaptive antenna systems.

A Statistical Spatio-Temporal Radio Channel Model for Large Indoor Environments at 60 and 70 GHz

Millimeter-wave radios operating at unlicensed 60 GHz and licensed 70 GHz bands are attractive solutions to realize short-range backhaul links for flexible wireless network deployment. We present a measurement-based spatio-temporal statistical channel model for short-range millimeter-wave links in large office rooms, shopping mall, and station scenarios. Channel sounding in these scenarios at 60 and 70 GHz revealed that spatio-temporal channel characteristics of the two frequencies are similar, making it possible to use an identical channel model framework to cover the radio frequencies and scenarios. The sounding also revealed dominance of a line-of-sight and specular propagation paths over diffuse scattering because of weak reverberation of propagating energy in the scenarios. The main difference between 60 and 70 GHz channels lies in power levels of the specular propagation paths and diffuse scattering which affect their visibility over the noise level in the measurements, and the speed of power decay as the propagation delay increases. Having defined the channel model framework, a set of model parameters has been derived for each scenario at the two radio frequencies. After specifying the implementation recipe of the proposed channel model, channel model outputs are compared with the measurements to show validity of the channel model framework and implementation. Validity was demonstrated through objective parameters, i.e., pathloss and root-mean-square delay spread, which were not used as defining parameters of the channel model.

Simulative Investigation of Flat Fading Wireless Channels in terms of First Order and Second Order Statistics

Second order statistics provides an efficient representation of a fading channel and plays a major role in the designing and evaluation of the wireless communication systems. This paper presents a channel model framework for the implementation of important second order statistical parameters, in particular the Level Crossing Rate (LCR) and the Average Fade Duration (AFD). In addition to this, other key first order statistics including Doppler Shift, CDF PDF, BER are also analysed. It also analyses the variations of LCR and AFD in multiple scattering environment .The analysis references to the well known Clarkes Model which has been utilised to plot the variations in key parameters.

Cooperative MIMO Channel Modeling and Multi-Link Spatial Correlation Properties

In this paper, a novel unified channel model framework is proposed for cooperative multiple-input multiple-output (MIMO) wireless channels. The proposed model framework is generic and adaptable to multiple cooperative MIMO scenarios by simply adjusting key model parameters. Based on the proposed model framework and using a typical cooperative MIMO communication environment as an example, we derive a novel geometry-based stochastic model (GBSM) applicable to multiple wireless propagation scenarios. The proposed GBSM is the first cooperative MIMO channel model that has the ability to investigate the impact of the local scattering density (LSD) on channel characteristics. From the derived GBSM, the corresponding multi-link spatial correlation functions are derived and numerically analyzed in detail.