Massive multiple-input multiple-output (MIMO) technologies have been greatly developed for the sixth-generation (6G) communication systems. Based on full-digital antenna arrays, asymmetric massive MIMO communication systems can provide distinct beamforming patterns for uplink and downlink chains. In this paper, a terahertz (THz) channel model for asymmetric massive MIMO communication systems considering the influence of different beam patterns is proposed. The proposed model incorporates channel characteristics of massive MIMO based on spherical-wavefront modeling and distance-dependent steering vectors. The asymmetry between uplink and downlink is considered through antenna beam patterns and array configurations. The statistical properties of the channel model are derived. These channel characteristics and system performance, such as channel capacity, are simulated and compared under different beam patterns. The results show that the space-time-frequency (STF) correlations increase while the delay spread and angular spread decrease when the beam patterns are more concentrated. It is also found that the channel capacity can be increased by utilizing high-gain and narrow beam patterns.

To address sparse channel measurement data and inadequate predictive capabilities in conventional channel models, predictive channel modeling employs joint generative and predictive architectures to enhance robustness. In this paper, we propose an enhanced diffusion-driven predictive framework that integrates generative augmentation and prior-aware prediction into a unified learning pipeline. We first introduce a space-time-frequency (STF) coupled diffusion network based on transformers that generates synthetic channel data preserving critical channel statistical properties. Additionally, we compress measured channel state information into a low-dimensional manifold via a latent encoder and introduce an innovative composite training scheme that couples diffusion-driven prior generation with prediction, equipping the predictive module with rich latent features that lift its performance ceiling and markedly improve generalization across diverse scenarios. Extensive experiments confirm the superiority of our algorithm, and its performance is further validated using channel measurement data, thereby demonstrating its robustness for advanced wireless communications in real-world deployment scenarios.

For future sixth generation (6G) space-air-ground-sea integrated networks, leveraging troposcatter for beyond line-of-sight (BLoS) propagation offers a promising solution to achieving long-range unmanned aerial vehicle (UAV) communications. In this paper, a three-dimensional (3D) multiple-input multiple-output (MIMO) geometry based stochastic model (GBSM) is proposed for long-range UAV-to-ground (U2G) communications. The proposed channel model incorporates UAV 3D mobility and troposcatter characteristics, enabling a distance-dependent transition from visual-range to BLoS communications. A path loss model that accounts for stochastic obstacle deployments and troposcatter characteristics is developed, and the corresponding link budget is calculated to evaluate the feasibility of BLoS communications. Furthermore, troposcatter clusters are modeled and their space-time evolution is characterized to capture channel non-stationarity. Statistical properties, including space-time-frequency correlation function (STF-CF), delay/Doppler power spectral densities (PSDs), root mean square (RMS) delay/Doppler spreads, and coherence bandwidth/time, are also derived and analyzed. The proposed channel model provides valuable guidance for the design of long-range U2G communications.

The integration of sub-6 GHz, centimeter-wave, and millimeter-wave bands through cross-band coordination is recognized as a pivotal approach to optimize system performance in the sixth generation (6G) research. The existing standard channel models, e.g., 3GPP TR 38.901 and QuaDRiGa, support cross-band channel simulations. However, both models assume that multipath components observed at different bands are identical, which is inconsistent with channel measurements. In this paper, we propose a general cross-band channel model, which considers the birth and death of clusters across different bands. The evolution of clusters in temporal and spatial domains is also incorporated, where the common clusters observed at different bands are modeled to undergo identical birth-death processes. Furthermore, the frequency-dependent blockage losses caused by obstacles are embedded in the proposed model. A comprehensive analysis of channel statistical properties is conducted, including the delay/angular/Doppler spreads and stationary times/distances. Moreover, the similarities of cross-band channels in angular and delay domains are analyzed. To validate the accuracy of the proposed model, cross-band channel measurements across 0.7 to 39 GHz are conducted by time-domain channel sounders, which are capable of simultaneously measuring channels at two bands. Comparisons of simulation results from the proposed model, enhanced 6G pervasive channel model, and 3GPP TR 38.901 with measurement data reveal that the proposed model demonstrates the best alignment with measurements.

In IoT-based underwater acoustic sensor networks (IoT-UASNs), critical challenges, such as inadequate node authentication mechanisms, unpredictable network topology, and ineffective localized node identification significantly hinder network performance. These challenges adversely impact node localization, route requests (RREQs) reconstruction at the beacon level, and lead to increased routing overhead, end-to-end delays, and unreliable data forwarding, thereby compromising underwater communication in critical areas of interest. Achieving reliable and efficient data transmission to sonobuoys while maintaining an optimal packet delivery ratio for localized nodes remains a significant priority. However, existing communication schemes often overlook the importance of authentication mechanisms, such as blockchain, in ensuring secure and efficient localization. Although previous research has focused mainly on generic localization strategies, the role of beacon-based localization, a key component in the formation of underwater networks, has been largely neglected. Despite the potential integration of beacon nodes with emerging technologies, such as blockchain, uncertainties persist about their ability to ensure secure underwater localization. This article addresses the pressing need to enhance the localization framework in AODV-based underwater networks by improving data delivery, optimizing RREQ and RREP processes, and minimizing both end-to-end delays and localization errors, thus paving the way for more reliable and secure underwater communication systems. In order to address these challenges, we first introduce a hybrid underwater localization communication framework based on the use of blockchain technology along with the proposed scheme, which is geomatric distance-based communication-based localization routing (CGDBLR). To establish the performance of this framework, we employ various node configurations and speeds with comparative experiments with state-of-the-art techniques. Our results show significant performance improvements for multiple parameters when integrating the blockchain in this context.

Indoor positioning is an important application of integrated sensing and communication technology in the sixth generation (6G) wireless communications. To address the limitations of existing fingerprint-based positioning methods (FPMs) under severe multipath effects, a novel channel state information (CSI)-based channel fingerprint structure and a novel line-of-sight (LoS)/non-LoS (NLoS) identification-assisted positioning method (IAPM) is proposed for 6G indoor multiple-input multiple-output (MIMO) communications. The proposed channel fingerprint structure uses the proposed maximum received power path to enhance the feature discrimination. The proposed IAPM incorporates a LoS/NLoS identification module and an improved weighted random forest (IWRF) positioning algorithm for accurate positioning. Evaluations on both channel measurement data and channel synthetic data generated by ray tracing demonstrate that the proposed method achieves superior accuracy and robustness compared with widely used FPMs.

Due to the agile three-dimensional (3D) mobility and flexibility, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications in sixth-generation (6G) wireless networks. In UAV communication systems, 3D beamforming is an effective technique for performance enhancement. To take full advantage of 3D beamforming in UAV communication systems, we need to accurately characterize and efficiently predict highly dynamic UAV channels in the beam domain. This paper first proposes a novel beam domain channel model (BDCM) considering random UAV trajectories and fuselage vibrations. Then, we propose a beam domain channel tracking algorithm to capture variations of multipath component (MPC) parameters in UAV channels. Finally, we put forward a novel beam domain channel prediction scheme for UAV communication systems utilizing channel sparsity and high temporal correlation in the beam domain. The proposed channel prediction scheme can extract channel variation trends utilizing the echo state network (ESN) and predict channel parameters in subsequent time blocks based on the history channel information. Simulation results show that the proposed channel prediction scheme outperforms the conventional prediction scheme based on angular speed estimation in terms of both prediction accuracy and communication system performance.

In this paper, a novel three-dimensional (3D) geometry-based stochastic model (GBSM) and a beam domain channel model (BDCM) for sixth-generation (6G) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) systems are proposed. The spherical wavefront and space-time-frequency non-stationarity introduced by massive MIMO, movements of the user and clusters, and large bandwidth of mmWave communications are incorporated. The shared clusters between the sensing channel and communication channel caused by the scattering characteristics are also considered. Based on the proposed channel model, important statistical properties are derived and simulated, including the space-time-frequency correlation function (STF-CF), root mean square (RMS) beam spread, RMS delay spread, RMS Doppler spread, coherence time, and channel capacity. By comparing the statistical properties of the sensing channel and the communication channel, it is found that the sensing channel exhibits more significant temporal non-stationarity. Moreover, the distribution of clusters for the sensing channel and communication channel shows significant difference, which is confirmed by the simulation results of RMS beam spread and RMS delay spread.

In this paper, we first propose a 3-dimensional (3-D) non-stationary geometry-based stochastic model (GBSM) for maritime massive multiple-input multiple-output (MIMO) communication systems with the uniform circular array (UCA) configuration. To reduce the model complexity and improve the mathematical tractability, a novel beam domain channel model (BDCM) is then proposed based on the transformation of the corresponding GBSM from the array domain to the beam domain for maritime communications. In the proposed BDCM, the beamforming matrices suitable for UCA structures are constructed and their invertibility is demonstrated to ensure the practicability of the BDCM. Two methods are used to characterize the array non-stationarity in maritime massive MIMO channels. First, the evolution of clusters over the large UCA is modeled by the visibility regions (VRs) attached to individual multipath components (MPCs). Second, the sphere wavefront (SWF) effect is captured by dividing the UCAs into several sub-arrays. Based on the proposed GBSM and BDCM, some important channel statistical properties are studied and compared, including channel power, power leakage, space-time-frequency correlation function (STF-CF), and root-mean-square (RMS) Doppler/beam spreads. Also, the importance of considering the array non-stationarity in maritime communication channels is revealed.

Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications in the next-generation wireless networks. Considering the massive multiple-input multiple-output (MIMO) configuration, this paper proposes a novel three-dimensional (3D) beam domain channel model (BDCM) for UAV communications. Through dividing the large antenna array into several sub-arrays and classifying multipath components as near-field and far-field components, the proposed BDCM takes the spherical wave front (SWF) and array non-stationarity into account. Channel statistical properties including spatial-temporal-frequency correlation function (STF-CF), root-mean-squared (RMS) Doppler spread, beam spread, channel matrix collinearity (CMC), and stationary time interval are derived and simulated for the proposed BDCM. Influences of SFW and non-stationary properties on the statistical properties and system performance are analyzed. Simulation results show that, compared with the equivalent geometry-based stochastic model (GBSM), the proposed BDCM has better temporal correlation, while BDCM and GBSM are equivalent in the system performance evaluation. Furthermore, the performance of the proposed BDCM is evaluated in terms of accuracy, complexity, and pervasiveness. The results show that the proposed BDCM can represent massive MIMO channel properties accurately with low complexity and good compatibility.

The integrated sensing and communication (ISAC) refers to the integration of radio sensing and wireless communications to realize the multiplexing of space, time, and frequency resources. In the sixth generation (6G) wireless networks, ISAC is considered as one of the most promising technologies. In this paper, a novel ISAC channel model combining forward and backward scattering is proposed. In addition to the non-stationarity caused by motions, the correlations between sensing and communication channels are investigated. The channel characteristics of sensing such as forward scattering and backward scattering are introduced into the communication channel model. Utilizing the correlations between sensing and communication channels, the communication channel model is divided into line-of-sight (LOS), forward scattering, and backward scattering components. These three components are summed according to probability weighting to obtain a more accurate channel model for sensing assisted communication systems. Moreover, important statistical properties of the proposed ISAC channel model are derived and simulated. The analytical and simulation results match well, demonstrating the correctness of derivations and simulations. Some derived/simulated statistical properties are verified by corresponding measurement data, which indicates the utility of the proposed ISAC channel model.

In this paper, a novel 3-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) is proposed to mimic the ship-to-ship multiple-input multiple-output (MIMO) communication channels. To reflect the realistic maritime propagation environment, the effects of rough sea surface scattering and evaporation duct propagation are investigated in the proposed channel model. The model considers the movements of transmitter (Tx), receiver (Rx), and scatterers, and is capable of capturing the channel characteristics, including non-stationary characteristics, spatial consistency, location-dependent property, etc. Through taking the array cluster evolution into account, the proposed channel model can describe massive MIMO channels and can easily switch to conventional MIMO channel model by adjusting corresponding parameters. Based on the proposed model, key statistical properties like delay/angular/Doppler power spectrum density (PSD), space-time correlation function (STCF), stationary interval, and root mean square (RMS) Doppler/delay spreads in multi-scenarios are derived. The usefulness and accuracy of the proposed model are demonstrated by comparing theoretical results, simulation results, and corresponding measurement results.

This paper proposes a novel quadrature space-frequency index modulation (QSF-IM) scheme as a promising energy-efficient radio-access technology for the fifth generation (5G) wireless systems. Motivated by the potential energy saving of spatial modulation (SM) with the part of information being carried through antenna indexes, the proposed scheme further leverages the benefits of SM by applying the idea across the spatial and frequency domains. Moreover, by deploying dual antenna constellation for in-phase and quadrature-phase transmission, the proposed scheme can enhance data rate at no extra cost of energy consumption, leading to further improvement in energy efficiency. Theoretical bit error rate and achievable sum-rate of the proposed scheme over frequency-selective correlated Rician and Rayleigh fading channels are derived and are shown to have good agreement with simulations. Furthermore, the effectiveness of the proposed scheme is analyzed through a comprehensive list of performance metrics, including spectral efficiency (SE), energy efficiency (EE), cost efficiency (CE), and economic efficiency. Performance trade-offs between these metrics are thoroughly investigated. Compared with other existing schemes, the proposed QSF-IM scheme is demonstrated to offer better EE-SE and EE-CE tradeoffs, and can therefore be considered as a potential candidate for energy-spectral efficient 5G systems.

A novel unified framework of geometry-based stochastic models 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, high-speed train, vehicle-to-vehicle, and millimeter wave communications. It is a 3-D non-stationary channel model based on the WINNER II and Saleh-Valenzuela 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.

Data collection from field sensors by using Unmanned Aerial Vehicle (UAV) is the application taken in consideration in this paper. All the sensor nodes are kept location unaware to reduce their cost and energy utilization. The issue addressed in this paper is localization of sensor nodes by UAV to collect data in efficient way. ULA of multiple antennas are used to measure the Angle of Arrival (AoA) of incoming signals. However, the drawbacks of mounting such multiple antennas on an Unmanned Aerial Vehicle (UAV) outweigh the benefits. The challenge is to affix multiple antennas and receivers on an UAV, increase its weight which ultimately decrease its payload capacity, flight time, speed and agility. In this paper, we are proposing a new method to estimate the AoA, called Virtual Phase Array (VPA) antenna system. A single moving antenna installed over an UAV taking snapshots every fixed time periods forms an antenna array virtually. This VPA has enable us to introduce two new concepts of adaptive staring precision and multiple frequency use. All these became reality only because number and spacing between antenna elements can be adjusted, which is not easy to implement in physical antenna array especially when antenna is onboard. The proposed system is evaluated by simulation model. Suggested modifications and additions in classical MUSIC algorithm make it possible to operate the virtual antenna system with the same precision as the physical antenna may have, but adding more flexibility, ease of use, cost economy, more reliability and better throughput.

In this paper, the bit error rate (BER) performance of a new multiple-input-multiple-output technique, named spatial modulation (SM), is studied under a novel non-stationary wideband high-speed train (HST) channel model in different scenarios. Time-varying parameters obtained from measurement results are used to configure the channel model to make all results more realistic. A novel statistic property called the stationary interval in terms of the space-time correlation function is proposed to describe the channel model's time-varying behavior. The accurate theoretical BER expression of SM systems is derived under the time-varying wideband HST channel model with the non-ideal channel estimation assumption. The simulation results demonstrate that the BER performance of SM systems shows a time-varying behavior due to the non-stationary property of the employed HST channel model. The system performance can maintain a relative stationary status within the specified stationary interval. It can also be observed that the BER performance of SM systems under the HST channel model is mainly affected by the correlation between sub-channels, inter-symbol-interference, Doppler shift, and channel estimation errors.

Autistic children often develop abnormal habits and in some cases they could be unsafe or even dangerous to themselves and their family members. Because of their limited speech ability, their inexperienced parents may underestimate their physical abilities compared to their intellectual level and may not realize that they could easily hurt themselves. Therefore, the need for an automatic alert system for autistic child parent assistance is great, and it will enhance the life experience for both the autistic child and the family. In this paper, we present a machine learning based electronic system for autism activity recognition using wireless sensor networks (WSNs). The system accurately detects autistic child gesture and motion. The system is named Autistic child Sensor and Assistant System (ACSA), and is comprised of three main components: the ACSA Wearable sensor device, the companion ACSA Parent Application and the machine learning algorithms developed for autistic movement event detection and processing. The paper describes the system concepts, its components and details of its architecture and operation. Individuals and families with Autism Spectrum Disorder children can utilize this system as alarming devices that assist them to protect their autistic child regardless of the environment. The proposed system is expected to enhance the life experience for all aides, the autistic child, the parents, and the autistic child whole family.

This paper proposes a novel method for ergodic sum rate analysis of spatial modulation (SM) systems with maximum likelihood receiver. This method is developed based on the MT-ary symmetric channel, where MTis the number of transmit antennas. The probability of antenna detection error is approximated by the pair-wise error probability. Then, an approximation to the ergodic sum rate of information transmission via SM with maximum likelihood receiver is computed. It is demonstrated via simulation that the proposed analysis method is able to provide an excellent approximation to the ergodic sum rate of SM.

This paper proposes a novel non-stationary wideband multi-confocal ellipse two dimensional (2-D) channel model for massive multiple-input multiple-output (MIMO) communication systems. 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 process is incorporated into the proposed model to capture the dynamic properties of clusters on both the array and time axes. Statistical properties of the channel model such as the space-time-frequency correlation function and power imbalance on the antenna array are studied. The impact of the spherical wavefront assumption on the statistical properties of the channel model is investigated. Furthermore, numerical analysis shows that the proposed channel model is able to capture specific characteristics of massive MIMO channel as observed in measurements.

Actual vehicle-to-vehicle (V2V) channel measurements have shown that the wide-sense stationary (WSS) modeling assumption is valid only for very short time intervals. This fact motivates us to develop non-WSS V2V channel models. In this paper, we propose a novel three-dimensional (3D) theoretical non-WSS regular-shaped geometry-based stochastic model (RS-GBSM) and the corresponding sum-of-sinusoids (SoS) simulation model for non-isotropic scattering wideband multiple-input multiple-output (MIMO) V2V fading channels. The movements of the transmitter (Tx), scatterers, and receiver (Rx) result in the time-varying angles of departure (AoDs) and angles of arrival (AoAs) that make our models non-stationary. The proposed RS-GBSMs, combining line-of-sight (LoS) components, a two-sphere model, and multiple confocal elliptic-cylinder models, have the ability to study the impacts of vehicular traffic density (VTD) and non-stationarity on channel statistics, and jointly consider the azimuth and elevation angles by using the von Mises Fisher (VMF) distribution. The proposed RS-GBSMs are sufficiently generic and adaptable to model various V2V scenarios. Based on the proposed 3D non-WSS RS-GBSMs, important local channel statistical properties are derived and thoroughly investigated. The impacts of VTD and non-stationarity on these channel statistical properties are investigated by comparing them with those of the corresponding WSS model. The proposed non-WSS RS-GBSMs are validated by measurements in terms of the channel stationary time. Finally, numerical and simulation results demonstrate that the 3D non-WSS model is more practical to characterize real V2V channels.

This paper analyzes the performance of space modulation techniques over generalized fading channels with imperfect channel estimation. In particular, a unified approach for calculating the pairwise error probability (PEP) of spatial modulation (SM) and space shift keying (SSK) modulation techniques for multiple-input multiple-output (MIMO) wireless communication systems is presented. A new, simple, and exact closed-form expression for the PEP over generalized α-μ fading channels under imperfect channel state information (CSI) is derived. The PEP expression considers the joint distributions of the envelope and the phase of the fading channel. Furthermore, the derived PEP and the union bound technique are used to obtain a closed-form expression for the average bit error rate (BER). The influence of the fading parameters α and μ and the channel estimation error on the system performance is analyzed and discussed through representative numerical examples. The correctness of our derivations is validated by means of MonteCarlo simulations.

This paper investigates the bit error rate (BER) performance of spatial diversity free-space optical (FSO) communication systems using on-off keying modulation. The study considers correlated log-normal FSO channels as well as path losses due to weather effects using intensity modulation and direct detection schemes. An approximated moment generating functions (MGF) for the joint probability density function of correlated log-normal channels are considered. Using MGF approximation, BER expressions for repetition codes (RCs) and orthogonal space time block codes (OSTBCs) in correlated lognormal channels are derived. Results show that RCs outperform OSTBCs in correlated channel conditions. In addition, the effect of different weather conditions (e.g., haze, rain and fog) on the BER performance of the FSO links are studied. Monte Carlo simulation results are further provided to demonstrate the validity of the proposed mathematical analysis.

This paper proposes a novel theoretical non-stationary three dimensional (3-D) wideband twin-cluster channel model for massive multiple-input multiple-output (MIMO) communication systems with carrier frequencies on the order of gigahertz (GHz). As the dimension of antenna arrays cannot be ignored for massive MIMO, near field effects instead of far field effects are considered in the proposed model. These include the spherical wavefront assumption and a birth-death process to model non-stationary properties of clusters such as cluster appearance and disappearance on both the array and time axes. Their impacts on massive MIMO channels are investigated via statistical properties including correlation functions, condition numbers, and angular power spectra. Additionally, the impact of elevation angles on correlation functions is discussed. A corresponding simulation model for the theoretical model is also proposed. Finally, numerical analysis shows that the proposed channel models are able to serve as a design framework for massive MIMO channel modeling.

This paper proposes a generic geometry-based stochastic model for nonisotropic scattering vehicle-to-vehicle (V2V) Ricean fading channels. With the proposed model, the level crossing rate (LCR) and average fade duration (AFD) are derived. The resultant expressions are sufficiently general and subsume many well-known existing LCRs and AFDs as special cases. The derived LCR and AFD are further investigated in terms of some important parameters, e.g., the shape of the scattering region (two-ring or ellipse), mean angle, angle spread, and directions of movement of the Tx and Rx (same or opposite direction). More importantly, in this paper, the impact of the vehicular traffic density on the LCR and AFD for nonisotropic scattering V2V Ricean fading channels is investigated for the first time. Excellent agreement is observed between the theoretical LCRs/AFDs and corresponding measured data, thus demonstrating the validity and utility of the proposed model.