Geometric Channel Model Research Articles

A Three-Dimensional Time-Varying Channel Model for THz UAV-Based Dual-Mobility Channels.

Unmanned aerial vehicle (UAV) as an aerial base station or relay device is a promising technology to rapidly provide wireless connectivity to ground device. Given UAV's agility and mobility, ground user's mobility, a key question is how to analyze and value the performance of UAV-based wireless channel in the terahertz (THz) band. In this paper, a three-dimensional (3D) time-varying channel model is proposed for UAV-based dual-mobility wireless channels based on geometric channel model theory in THz band. In this proposed channel model, the small-scale fading (e.g., scattering fading and reflection fading) on rough surfaces of communication environment and the atmospheric molecule absorption attenuations are considered in THz band. Moreover, the statistical properties of the proposed channel model, including path loss, time autocorrelation function (T-ACF) and Doppler power spectrum density (DPSD), have been derived and the impact of several important UAV-related and vehicle-related parameters have been investigated and compared to millimeter wave (mm-wave) band. Furthermore, the correctness of the proposed channel model has been verified via simulation, and some useful observations are provided for the system design of THz UAV-based dual-mobility wireless communication systems.

A Novel UAV Air-to-Air Channel Model Incorporating the Effect of UAV Vibrations and Diffuse Scattering

In this paper, we propose a geometric channel model for air-to-air (A2A) unmanned aerial vehicle (UAV) communication scenarios. The model is established by incorporating line-of-sight, specular reflection, and diffuse scattering components, and it can capture the impacts of UAV vibrations induced by the propeller’s rotation. Based on UAV heights and ground scatterer density, a closed-form expression is derived to jointly capture the zenith and azimuth angular distributions of diffuse rays. The power of diffuse rays is modeled according to the grazing angle of the rays and the electrical properties and roughness of the ground materials. Key statistics, including the temporal autocorrelation function, spatial cross-correlation function, Doppler power spectrum density, and coherence time are derived, providing an in-depth understanding of the time-variant characteristics of the channel. The results indicate that the presented model is capable of capturing certain A2A channel characteristics, which align with the corresponding theoretical analysis. The findings suggest that the scattering effect of the A2A channel is significantly influenced by the altitude of the UAV. Additionally, it is shown that UAV vibrations can introduce extra Doppler frequencies, notably decreasing the temporal correlation and coherence time of the channel. This effect is more prominent when the system operates at high-frequency bands. The effectiveness of the presented model is confirmed through a comparison of its statistics with those of an existing model and with available measurement data.

3D Antenna array for multi-user massive MIMO system to minimize unfavorable propagation

Unfavorable propagation in massive multiple input multiple output (MIMO) systems is an underlying channel condition that reduces the gains offered by the system in terms of parallel channels. This paper presents a study on different types of antenna array configurations at base station (BS) to minimize unfavorable propagation conditions. Unfavorable propagation results in increased correlation between channel vectors of two or more mobile terminals (MTs) which arises when the signals from two or more MTs have small angular separation and small angular spread. The study starts with the investigation of the conventional uniform linear array (ULA) under different propagation conditions using a geometrical channel model based on a single cluster of scatterers per MT to quantify the performance degradation in unfavorable propagation. Subsequently, the performance of other types of array geometries is investigated. A custom cluster-based geometrical channel model is used in this study which includes practical propagation aspects like multipath fading, shadow fading, and the effect of BS height. Further, the study considers two cases: micro-cell and macro-cell. Under space constraint settings in terms of the maximum dimension of the array, the proposed technique uses randomized antenna locations in the array to minimize the effect of unfavorable propagation. Based on the results on performance parameters like user correlation and capacity, the study reveals that an antenna array configuration comprising of elements distributed random-homogeneously in a spherical shell outperforms other configurations.

Verification and Modeling of the Maritime Channel for Maritime Communications and Navigation Networks

This paper presents a geometric stochastic channel model designed for analyzing maritime communication and navigation services between moving ships using the C-band or sub-6 GHz spectrum, which aligns with the focus of emerging 5G networks on land. The channel model is validated through channel measurements conducted both on the sea and land. A software tool has been developed to integrate and analyze these measurements, which is included with this publication. The main challenge in developing the channel model for maritime services lies in the dynamic nature of the sea surface, leading to constantly changing reflection conditions due to varying reflectors and scatterers on the water. Additionally, the motion conditions of the transmitter and receiver on ships change in all three dimensions, depending on the sea state. To address these complexities, data from several measurement campaigns in diverse areas were collected. The analysis involved examining the propagation conditions over the sea with variations in sea surface roughness, antenna heights, and used bandwidths. Moreover, additional propagation conditions over nearby land were also taken into account. The study demonstrates that the changing antenna height on the ship, influenced by sea conditions, significantly affects the reflection and scattering conditions. The research aims to develop reliable, high-data rate, and broadband marine communication systems. Therefore, a measurement bandwidth of 120MHz was employed to derive the propagation model. This model not only offers absolute timing information but can also be used for time-based ranging or positioning systems. The proposed geometric stochastic channel model provides valuable insights into the complex maritime communication and navigation environment. By accounting for the continuously evolving sea surface and its impact on antenna height, the model offers a robust framework for studying and optimizing marine communication systems. The availability of a software tool integrating real-world measurements further enhances the usability and practicality of the channel model for future maritime communication research and deployment.

Diffuse-Scattering-Informed Geometric Channel Modeling for THz Wireless Communications Systems

Diffuse-Scattering-Informed Geometric Channel Modeling for THz Wireless Communications Systems

An Enhanced Approach to Virtually Increase Quasi-Stationarity Regions Within Geometric Channel Models for Vehicular Communications

Vehicular communication channels are intrinsically non-stationary, as they present high mobility and abundant dynamic scatterers. Quasi-stationary regions can assess the degree of non-stationarity within a determined scenario and time variant observation of the channel can be extracted. These regions can aid geometrical models as to increase channel sampling intervals or to develop hybrid stochastic-geometric channel models. In this work, a new methodology for the use of virtual quasi-stationary regions within geometric channel models is proposed, in order to leverage the inherent location information to virtually increase their size. Overall, the use of delay-shifted channel responses improves the mean correlation coefficient between consecutive locations, ultimately reducing computation time for time-variant geometric channel models.

РАЗРАБОТКА НИЗКОРАНГОВЫХ ПРОСТРАНСТВЕННЫХ ВЕКТОРОВ ПРЕДВАРИТЕЛЬНОГО КОДИРОВАНИЯ ВО ВРЕМЕННОЙ ОБЛАСТИ

In this paper we consider the alternative way of development of spatial beamforming processing algorithm in time-domain under low-rank Massive-MIMO systems. It is well-known, that the telecommunication systems, which based on spatial multiplexing technology achieve following advantages: increase the spectral efficiency in many times due to spatial channel multiplexing gain, the beamforming technology implementation (effective energy distribution in the space). The organization of spatial filtering (the beamforming vector processing) – one of the most significant tools in MIMO-OFDM multiplexing. In this paper, we consider a new spatial filtering processing approach (precoding vectors calculation), which is a significant tool in large-scale antenna systems, due to the fact of smart processing of the radio narrow-beam implementation increases the efficiency of the communication system many times over. Nowadays, the spatial filter design algorithms with low computational complexity have attracted considerable attention from researchers seeking to improve existing MIMO-OFDM wireless communication systems. In the paper we developed basic algorithms for calculating precoding vectors in the frequency domain and proposed the time-domain precoder approach with aim to achieve the complexity reduction gain. The proposed method was tested on the geometric statistical simulation channel model QuAdRiGa according to the multipath channel scenario "3GPP-3D Uma-NLoS". We provide the assessment of whole presented methods and the evaluation range of discrepancies was revealed in the form of pictures of the correlation and integral metrics. The analysis of integral cumulative distribution function of simulation results shows the relation gap between accuracy of correlation metric and losses of beamforming processing recalculation approximately -0.5 dB for 1st layer. In conclusion of paper was revealed the fact that proposed SVD factorization for each impulse response did not take into account the beams and eigenvectors distribution over the whole frequency bandwidth

A Visible Human Data-Based Whole-Body Model for Investigating the Transmission Attenuation of Intrabody Communication

Intrabody communication (IBC) is a promising approach for realizing information transmission among various wearable or implantable devices within bodyNET. The existing IBC studies are limited to the local or abstract geometric channel model, which is limited to providing efficient channel characteristics for the various transmitting scenarios. The aim of this article is to reconstruct a whole-body model with anatomical features and combine the field-circuit model for investigating the human channel characteristics. A visible human data (VHD) is established by segmentation and 3-D reconstruction of layered tissues. The transmission attenuation was simulated and analyzed through the finite element method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In vivo</i> experiments and phantom experiments were implemented to validate the simulation results. For the on-body communication, the transmission attenuation of the short distance was similar to the low-pass filter’s amplitude-frequency characteristics. For the implantable communication, the consistency coefficient of the attenuation between in-body to on-body and on-body to in-body was higher than 98%, indicating that the body channel is bidirectional. This work provides a repeatable and verified VHD-based whole-body model for the human channel attenuation investigation. It is an essential step for the further development of IBC, such as the transceiver design and the multinodes’ protocol for bodyNET.

Unfolded Hybrid Beamforming With GAN Compressed Ultra-Low Feedback Overhead

Optimizing a hybrid beamforming transmitter is a non-convex problem and requires channel state information, leading in most cases to nontrivial feedback overhead. We propose a methodology relying on the principles of deep generative models and unfolding to achieve near-optimal hybrid beamforming with reduced feedback and computational complexity. We first represent the channel as a low-dimensional manifold via a generative adversarial network (GAN) and search the optimum digital and analog precoders in this low-dimensional space. To decrease the search complexity, we find an iteration rule by formulating hybrid beamforming as a bi-level optimization problem and then unfold each iteration as a neural layer. This results in a novel model-based deep neural network that incorporates domain knowledge. Our results show that this method (i) approaches the capacity-achieving spectral efficiency, (ii) provides a superior energy and spectral efficiency tradeoff, (iii) decreases feedback overhead, and (iv) reduces the complexity significantly, by optimizing a single low-dimensional vector per channel coherence time, with the neural network itself trained offline. The achieved spectral efficiency is robust when tested with realistic 3GPP channel models, even if the offline training relies on a simple geometric channel model.

Iteratively reweighted super-resolution channel estimation in hardware-impaired hybrid-precoded MM-wave massive MIMO systems using dual SVD and Marquardt's global search

In this paper, a three-dimensional geometric channel model characterized by channel gain, angle of arrival, and departure (AoA/AoD) is used to develop a non-ideal and more realistic system representation that accounts for additional signal perturbations other than additive white Gaussian noise (AWGN). Hinged on this, compressive sensing (CS) based channel estimation technique is proposed for millimeter-wave (MM-Wave) massive multiple-input multiple-output (MIMO) systems with inherent challenges due to hardware impairment (HI). The proposed estimator named dual singular value decomposition (SVD) and Marquardt, abbreviated as DSM estimator aims at tolerating complexity and improving channel estimation accuracy at reasonable trade-offs in the system. The normalized mean square error (NMSE) performance of the proposed channel estimation scheme, with modified Marquardt's global minimum search, achieves higher accuracy at below 0 dB (LoS)/-3 dB (NLoS) signal-to-noise ratio (SNR) for uniform linear array (ULA) configuration and performs better for uniform planar array (UPA) configuration. A low-rank range of unit difference is adopted for matrix decomposition twice (dual SVD) per SNR in ULA/UPA setting. The proposed estimator's result shows early convergence during the simulation and a linearly scaled complexity in comparison with one of the earlier proposed schemes in literature in particular the super-resolution channel estimation with gradient descent optimization.

Angle-of-arrival and time-of-arrival statistics of multipaths arising from scatterers on an elliptical disc with conic spatial density around the mobile

This paper presents the angle-of-arrival (AOA) and time-of-arrival (TOA) statistics of multipaths arriving from scatterers with conic spatial density and located on an elliptical disc around the mobile. For simplification of analysis, a common practice in geometric channel modeling of macrocell networks in the open literature is to assume the scatterers to be located on a circular disc around the mobile for non-uniform scatterers’ spatial densities. This paper extends the analysis for scatterers with conic spatial density on a circular disc around the mobile to that on an elliptical disc around the mobile. The AOA-TOA joint probability densities and AOA marginal probability densities from the base station and mobile station sides were derived analytically in closed form. The TOA marginal probability densities for both the base-station and mobile-station sides were also studied. The effect of the eccentricity of the elliptical disc on the derived probability densities was discussed. In most cases, increase in the eccentricity results to reduction in the AOA support region.

Modeling of Downlink Interference in Massive MIMO 5G Macro-Cell.

Multi-beam antenna systems are the basic technology used in developing fifth-generation (5G) mobile communication systems. In practical implementations of 5G networks, different approaches are used to enable a massive multiple-input-multiple-output (mMIMO) technique, including a grid of beams, zero-forcing, or eigen-based beamforming. All of these methods aim to ensure sufficient angular separation between multiple beams that serve different users. Therefore, ensuring the accurate performance evaluation of a realistic 5G network is essential. It is particularly crucial from the perspective of mMIMO implementation feasibility in given radio channel conditions at the stage of network planning and optimization before commercial deployment begins. This paper presents a novel approach to assessing the impact of a multi-beam antenna system on an intra-cell interference level in a downlink, which is important for the accurate modeling and efficient usage of mMIMO in 5G cells. The presented analysis is based on geometric channel models that allow the trajectories of propagation paths to be mapped and, as a result, the angular power distribution of received signals. A multi-elliptical propagation model (MPM) is used and compared with simulation results obtained for a statistical channel model developed by the 3rd Generation Partnership Project (3GPP). Transmission characteristics of propagation environments such as power delay profile and antenna beam patterns define the geometric structure of the MPM. These characteristics were adopted based on the 3GPP standard. The obtained results show the possibility of using the presented novel MPM-based approach to model the required minimum separation angle between co-channel beams under line-of-sight (LOS) and non-LOS conditions, which allows mMIMO performance in 5G cells to be assessed. This statement is justified because for 80% of simulated samples of intra-cell signal-to-interference ratio (SIR), the difference between results obtained by the MPM and commonly used 3GPP channel model was within 2 dB or less for LOS conditions. Additionally, the MPM only needs a single instance of simulation, whereas the 3GPP channel model requires a time-consuming and computational power-consuming Monte Carlo simulation method. Simulation results of intra-cell SIR obtained this way by the MPM approach can be the basis for spectral efficiency maximization in mMIMO cells in 5G systems.

3D deterministic ray tracing method for massive MIMO channel modelling and parameters extraction

Geometric stochastic method and deterministic ray tracing method are two common methods of modelling massive multiple-input multiple-output (MIMO) channels. The former has high computational efficiency but a large number of input parameters need to be extracted from measurements for different environments. Conversely, the latter is more suitable for a specific environment but consumes a lot of computing costs. In this study, a novel three-dimensional (3D) deterministic ray tracing method for massive MIMO channel modelling is proposed. The computational efficiency can be improved in two aspects compared with conventional deterministic ray tracing methods. Firstly, substantial intersection tests used in determining the propagation paths of rays are replaced with the adjacency relationships between tetrahedrons. Secondly, the process of ray tracing is independent of the location of a receiving antenna and therefore repeated ray tracing process is unnecessary for different elements of receiving antenna array. The proposed method is also used as a substitute for measurements to extract input parameters for geometric stochastic channel models. The accuracy of the proposed method in massive MIMO channel modelling and parameters extraction is verified by comparing the results with measurements and other existing channel models.

Deep Learning for SVD and Hybrid Beamforming

Hybrid beamforming (BF), which divides BF operation into radio frequency (RF) and baseband (BB) domains, will play a critical role in MIMO communication at millimeter-wave (mmW) frequencies. In principle, we can obtain unconstrained (optimum) beamformers of a transceiver, which approach the maximum achievable data rates, through its singular value decomposition (SVD). Due to the use of finite-precision phase shifters, combined with power constraints, additional challenges are imposed on the problem of designing hybrid beamformers. Motivated by the recent success of machine learning (ML) techniques, particularly in areas such as computer vision and speech recognition, we explore if ML techniques can be effectively used for SVD and hybrid BF. To this end, we first present a data-driven approach to compute the SVD. We propose three deep neural network (DNN) architectures to approximate the SVD, with varying levels of complexity. The methodology for training these DNN architectures is inspired by the fundamental property of SVD, i.e., it can be used to obtain low-rank approximations. We next explicitly take the constraints of hybrid BF into account (such as quantized phase shifters, power constraints), and propose a novel DNN based approach for the design of hybrid BF systems. To validate the DNN based approach, we present simulation results for both approximating the SVD as well as for hybrid BF. Our results show that DNNs can be an attractive and efficient solution for estimating SVD in a data-driven manner. For the simulations of hybrid BF, we first consider the geometric channel model. We show that the DNN based hybrid BF improves rates by up to 50 - 70% compared to conventional hybrid BF algorithms and achieves 10 - 30% gain in rates compared with the state-of-art ML-aided hybrid BF algorithms. We also discuss the impact of the choice of hyperparameters, such as the number of hidden layers, mini-batch size, and training iterations on the accuracy of DNNs. Furthermore, we provide time complexity and memory requirement analyses for the proposed approach and state-of-the-art approaches.

3‐D Massive MIMO Channel Model for High‐Speed Railway Wireless Communication

AbstractIn the fifth generation (5G) wireless communication, high‐speed railway (HSR) communication is one of the most challenging scenarios. By adopting massive multi‐input multioutput (mMIMO) technology in HSR communication, the design of the underlying communication system becomes more challenging. Some new channel characteristics must be studied, such as nonstationarity in space, time, and frequency domains. In this paper, two models are established for the two states of HSR. When the HSR stops, a three‐dimensional (3‐D) stationary channel model based on the single‐ring distribution of the scatterers is established. When the HSR moves, a 3‐D nonstationary geometric random channel model (GRCM) based on the cylinder distribution of the scatterers by introducing the birth and death process of the propagation path and time‐varying characteristics of the channel parameters is established. Moreover, a time evolution algorithm for time‐varying channel parameters is proposed, and a modularization study of key parameter update algorithms is conducted. Finally, based on the proposed 3‐D nonstationary geometric random channel model, important time‐varying channel statistical characteristics, such as time autocorrelation function (ACF), spatial cross‐correlation function (CCF), Doppler power spectral density (DPSD), and stationary interval are studied. Simulation results indicate that the properties of the proposed channel models, verified by simulation results, can match well with those of the theoretical model.

Adaptive Sum of Markov Chains for Modelling 3D Blockage in mmWave V2I Communications

Blockage attenuation is one of the main challenges that face reliable communication at the millimetre-wave (mmWave) band, especially for dynamic vehicle-to-infrastructure (V2I) communication systems. Modelling the dynamics of the blockage is important for evaluating high gain beamforming techniques, which are used to improve the signal strength in the mmWave communication systems. The novel sum of Markov chains (sum of MC) model is designed to do two main tasks successfully; capturing the dynamics of blockers affecting a moving transceiver and computing the arising channel attenuation. The sum of MC model has advantages over existing Markov chains models, which are: that it can adapt to model non-stationary scenarios. The sum of MC model can integrate any attenuation function, including the 3GPP blockage model and any lab measurement attenuation profile. Additionally, it is computationally efficient. The sum of MC model can match well with the performance results from a more complex geometric channel model.

Relay Node Placement in Wireless Sensor Networks: From Theory to Practice

The increasingly wide utilization of Wireless Sensor Networks (WSNs) in industrial applications outstands the significance of the Delay Constrained Relay Node Placement (DCRNP) problem. Existing algorithms to the DCRNP problem are designed based on the ideal geometric disk wireless channel model, and no real-world deployments are performed to verify the effectiveness of these algorithms. However, the unreliable and unpredictable wireless links in WSNs may lead these algorithms to fail in practice. Therefore, we first conduct extensive real-world deployments under the guidance of existing algorithms to evaluate their performance and to gain some insights for designing practical deployment algorithms. The results exhibit that the WSNs built by existing algorithms have a favorable performance in end-to-end delay but a poor performance in reliability, which is mainly due to the lack of methods ensuring high-quality links. To this end, we first devise a Set-Covering-based Algorithm (SCA) which figures out the DCRNP problem while ensuring the quality of each link better than a given threshold. As our experiments also show that the fault-tolerant topology can significantly improve network reliability, we then design a $k$ k -Set-Covering-based Algorithm ( $k$ k SCA) to build fault-tolerant WSNs based on the methodology of SCA. Furthermore, the elaborate analysis proves that both SCA and $k$ k SCA are polynomial-time algorithms, and their approximation ratios are both O( $\ln n$ ln n ), where $n$ n is the number of sensor nodes. Finally, extensive experiments are performed under the guidance of SCA and $k$ k SCA to demonstrate the effectiveness of these two algorithms.

Fast in-Band Position-Aided Beam Selection in Millimeter-Wave MIMO

Millimeter-wave (mm-wave) and massive multiple-input-multiple-output technologies appear as key enablers in future emerging wireless communication systems. Propagation in mm-wave communications is well described by geometric channel models, where a clear relation between communication and positioning arises. On one hand, initial access, a procedure that precedes high data rate transmission and consists of beam selection and alignment between two devices, can benefit from position information. On the other hand, accurate positioning relies on high-quality communication links with proper beam alignment. This work analyzes the interplay between communication and positioning, proposes a new in-band position-aided beam selection protocol considering scenarios with line-of-sight and reflected paths, and possible beam alignment errors. Simulation results show significant reductions in latency with respect to standard beam selection protocols.

Rational-Orthogonal-Wavelet-Based Active Sonar Pulse and Detector Design

A family of rational orthogonal wavelets is explored in active sonar detection to achieve Doppler robustness and noise mitigation. The broadband active sonar system is classified as wavelet-based noncoherent subband energy detection. It features a flexible broadband sonar pulse design with low peak-to-average power ratio (PAPR), and a detector of low computation complexity via fast rational wavelet filter bank. The designed family of broadband pulses is based on a combination of multiple basis functions of the complex rational orthogonal wavelet (CROW). Differing from the conventional dyadic wavelet (with an integer scale factor of 2), the CROW has a rational scale factor and much higher resolution in the partition of the time-scale plane. This family of wavelets presents great flexibility in the system design. Design example is presented with minimized PAPR to serve for applications demanding the stealth. The sonar detector is based on the CROW analysis filter bank. Detection performance is analyzed regarding its resistance to Doppler noise and multipath by simulations using a geometric underwater acoustic channel model. The performance of the wavelet-based active sonar system is compared with a linear frequency modulated pulse-based system. The receiver operating characteristic curves demonstrate that the designed system is able to cope with severe multipath, high ambient noise, and severe Doppler dispersion. The framework provides possible solutions for underwater applications in low signal-to-noise ratio and varying mobility conditions.

A Spatially Consistent MIMO Channel Model With Adjustable K Factor

In the area of research on massive multiple-input multiple-output (MIMO), two assumptions on the wireless channel dominate channel modeling. Either, a rich scattering environment is assumed and the channel is modeled as i.i.d. Rayleigh fading, or, a line of sight (LOS) channel is assumed, enabling geometric channel modeling under a farfield assumption. However, either of these assumptions represents an extreme case that is unlikely to be observed in practice. While there is a variety of MIMO channel models in literature, most of them, and even very popular geometry based stochastic channel models, are not spatially consistent. This is especially problematic for technologies in which channel correlation of adjacent users is an important factor, such as massive MIMO. In this work, we introduce a simple but spatially consistent MIMO channel model based on multiple scattering theory. Our proposed channel model allows to adjust the Rician $K$ factor by controlling the number and strength of scattering elements. This allows to perform spatially consistent simulations of wireless communications systems for a large range of scattering environments in between an i.i.d. Rayleigh fading assumption and pure LOS channels. A statistical analysis in terms of the Rician $K$ factor for the introduced model is provided and verified by simulations. By comparison to other channel models, we show that non spatially consistent channel models lead to an underestimation of inter-user correlation and therefore to an overestimation of achievable sum rate.