In this paper, we propose an aerial reconfigurable intelligent surface (ARIS)-enhanced unmanned aerial vehicle (UAV)-to-vehicle channel model that considers a three-dimensional (3D) continuously arbitrary trajectory. The model uses an ARIS mounted on a UAV to reflect signals to the terrestrial vehicle, which facilitates and enhances signal propagation. A generalized 3D random mobility model (RMM) with smooth turns is developed to characterize ARIS kinematics, where state differential equations and Euler approximation are employed for low-complexity real-time trajectory updates. Moreover, we model both the ARIS translational jitter and attitude jitter caused by wind or air turbulence as zero-mean Gaussian random variables, investigating the sensitivity of channel characteristics to assess the system robustness under non-ideal control conditions. The resulting trajectory and jitter models capture the unique channel properties in realistic scenarios. Furthermore, key statistical properties of the proposed channel model are derived, including spatial-temporal (ST) cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency correlation functions (FCFs). We analyze the impact of ARIS trajectory and physical parameters on these statistical properties, such as velocity magnitude, phase shifts design, unit numbers, and rotation angles. Numerical simulation results demonstrate the superiority of ARIS-enhanced UAV communications using the proposed generalized 3D smooth-turn RMM with both translational jitter and attitude jitter, highlighting the significant role of ARIS in UAV communications.

In this paper, we propose a three-dimensional (3D) geometry-based stochastic model (GBSM) for double-reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) air-to-ground (A2G) communication systems. We develop the GBSM for dual-RIS channels, where RIS arrays are strategically mounted on unmanned aerial vehicles (UAVs) to reflect signals from the UAV transmitter towards the ground receiver via cascaded RIS links. The flexible trajectories and on-demand deployment of UAVs effectively mitigate the degradation caused by obstructive elements like buildings and trees. Furthermore, we incorporate a beam-domain channel model (BDCM) into the geometric framework to systematically analyze its propagation framework. This approach reduces computational complexity and enables systematic analysis of the system’s propagation mechanisms. The model captures dynamic behaviors in realistic scenarios by integrating real-time kinematic parameters, including velocities and accelerations of the UAV transmitter, ground receiver, and RIS-mounted UAVs. Key propagation characteristics, such as cross-correlation functions (CCFs), autocorrelation functions (ACFs), frequency correlation functions (FCFs), and channel capacity, are analyzed by comparing the proposed beam-domain approach with conventional geometric methods. Simulation results demonstrate that the statistical properties obtained from the beam-domain channel model closely match those derived from the geometry-based stochastic model, validating the accuracy of the proposed approach. Moreover, the beam-domain method significantly reduces computational complexity over traditional geometric techniques, offering valuable insights for designing efficient distributed RIS-assisted A2G communication systems.

Low-light video enhancement is crucial for improving the visual reliability of IoT edge devices in low-light environments. However, existing methods often rely on complex network architectures, require strictly curated data, or complex preprocessing computation, resulting in poor real-time performance and limited generalization. We propose an unsupervised low-light video enhancement framework named ViD-EnlightenGAN. By incorporating temporal attention mechanisms and multi-discriminator constraints, our method achieves inter-frame consistency preservation and dynamic brightness adjustment without complex pre-processing such as optical flow or keyframe matching. Experiments demonstrate that our method achieves outstanding performance on the SDSD dataset (PSNR: 23.711 dB, SSIM: 0.695), delivering high visual quality and temporal consistency. The code will be available at https://github.com/ apperrs/ViD-EnlightenGAN.

Optical reconfigurable intelligent reflecting surface (ORIS), as a new type of programmable optical communication equipment, can reconstruct the optical channel environment and expand the application scenarios of optical wireless communications (OWC), which have attracted widespread attention. However, with the increasing demand for OWC scale in B5G and even 6G scenarios, the coverage range of a single ORIS (below 300 m) can no longer meet communication needs. Cascaded ORISs’ technology has become an inevitable trend in the development of ORIS, which has not been deeply studied in existing work. To address the above challenges, in this paper we set up cascaded ORISs in a typical long-distance free space optcis (FSO) scenario and conduct a detailed analysis of their channels and performance. This system employs cascaded ORISs to extend ORIS coverage and expand the overall scale of the FSO system, thereby enabling the FSO link to evolve from a traditional point-to-point configuration to a broader, spatially distributed communication layout. The physical model of multiple ORISs’ cascades and the effect on their communication performance are analyzed in detail. Meanwhile, the closed-form expressions of the bit error rate (BER) and outage probability of the multi-hop cascaded ORISs-assisted FSO system are derived and verified by simulations. Based on the theoretical results and simulation results, the influence of each parameter on the system performance is carefully analyzed, which provides guidance for the design of the actual system.

Optical reconfigurable intelligent reflecting surface (ORIS), as a new type of programmable optical communication device, can deflect, split, and shape incident light, thereby intelligently reconstructing the optical wireless communication (OWC) environment. The application of ORIS has effectively expanded the application scenarios of OWC and improved system performance and robustness, which has attracted widespread attention. However, the existing ORIS is limited by its basic structure, and has problems such as low stability and poor signal quality in long-distance transmission. Aiming at the pain points of the existing ORIS, this paper proposes an optical reconfigurable distributed sources and reflecting surface (ORDSS) for distributed OWC. By arranging distributed sources in partial areas of the metasurface, users’ performance optimization is achieved based on power compensation and beam adaptive optimization algorithms. In this work, we performed physical modeling of an ORDSS-assisted OWC system, incorporating factors such as the characteristics and spatial distribution of ORDSS sources and reflection units, surface jitter of the ORDSS, pointing errors, and atmospheric attenuation. We derived the expression for the receiving performance of the ORDSS-assisted OWC system and compared it with that of conventional ORIS-assisted OWC systems to evaluate the performance gains brought by ORDSS. Building on the physical model and performance analysis, we investigated an adaptive power control algorithm for the distributed sources in ORDSS. In addition, we explored power compensation mechanisms based on ORDSS distributed sources to enhance system performance in mobile communication scenarios.

The integration of movable antenna (MA) technique into unmanned aerial vehicle (UAV) communications offers a promising solution to reliable and energy-efficient non-terrestrial networking for low-altitude economy. To characterize multi-MA-assisted UAV-to-UAV wideband fading channels, we propose a three-dimensional arbitrary-elevation two-concentric-cylinders reference model. Based on this model, we derive the space-time-frequency correlation function (STF-CF) in closed form. From the STF-CF, we also obtain the space-Doppler power spectral density (SD-PSD) and the power space-delay spectrum (PSDS). The excellent agreement between the theoretical PSDS and some previously reported measurement data demonstrates the utility of the reference model. We then establish corresponding simulation models, which produce consistent results with the derived expressions. By leveraging the closed-form correlation function, the gradient of the log-determinant of spatial correlation matrix with respect to the MA positions can be conveniently obtained, which may serve as a method to maximize the ergodic capacity of the multi-MA assisted wideband UAV-to-UAV channels.

This paper investigates fluid antenna systems (FASs) subject to finite-blocklength (FBL) constraints, motivated by the strict reliability-latency and ultra-massive connectivity requirements of future wireless networks. While FAS performance has been widely studied in the asymptotic regime, its behavior under FBL remains largely unexplored. Our objective is to develop a unified set of analytical tools for evaluating FASs under FBL that remains applicable across different spatial-correlation models. First, to establish accurate benchmarks for non-orthogonal finite-length user signature design, we characterize both the average and the worst-case correlation coefficients via extreme value theory (EVT) and derive closed-form predictions of the achievable correlation levels. Second, taking block error rate (BLER) as the fundamental FBL metric, we study joint detection and decoding in FAS-assisted links and derive a closed-form BLER expression that is universally applicable across channel models. Additionally, we revisit outage probability (OP) in the FBL regime and obtain tractable OP characterizations for both FASs and conventional multiple fixed-position antenna (FPA) systems. In order to reduce the computational burden for multi-fold integrals in correlated fading models, we further propose a Taylor-expansion-assisted mean value theorem for integrals (MVTI), thus enabling efficient performance evaluation with marginal accuracy loss. Numerical results validate the analysis and reveal that even single-antenna FASs can have superior spatial diversity relative to conventional multi-FPA systems. Moreover, under both FBL and interference-limited environments, FASs provide improved energy, spectral, and hardware efficiencies, hence highlighting FAS as a promising enabler for next-generation wireless networks.

Abstract To address the issue of low coverage resulting from the challenge of acquiring the optimal deployment position in commonly used distributed deployment algorithms, this study presents a three‐dimensional deployment algorithm for Unmanned Aerial Vehicles (UAVs) based on potential games. First, a local mutually beneficial game model is designed to demonstrate the existence of exact potential games and Nash equilibrium. The Nash equilibrium solution corresponds to the maximum coverage. Next, drawing inspiration from exploration, a solution method called Exploration Spatial Adaptive Play is proposed. It utilizes the maximum utility function value from multiple step sizes in the exploration direction to update the action selection probability, thereby ensuring the optimal deployment position in each decision cycle. To address the issue of sensor position error, a method for processing sensor position errors is proposed. The simulation results demonstrate that the proposed distributed deployment algorithm achieves higher coverage compared to commonly used methods.

In future networks, the integrated sensing, communication and computation (ISCC) has gradually become a research hotspot. In this paper, we investigate a novel computation resource allocation scheme for reconfigurable intelligent surfaces (RIS) backscatter communication (BackCom)-aided ISCC system. We consider the joint design of transmit beamforming at BS and the reflecting coefficients at RIS as well as the computation resource allocation of each user. The optimization problem for the max-min computation efficiency (CE) under the constraints of power consumption, the Cramér-Rao bound (CRB) for angles estimation and communication requirement of each user is formulated. To deal with the intractable optimization problem, the block coordinate descent (BCD) algorithm is utilized to tackle the joint optimization problem. We propose the penalty function-based successive convex approximation (SCA) method to optimize the reflecting coefficients and the majorization-minimization (MM) framework to design the transmit beamforming, respectively. In addition, considering the high complexity of the proposed SCA based algorithm, we design a low-complexity beamforming and reflection coefficient scheme for a special case of single target scenario. Simulation results show that the introduction of RIS-BackCom can improve the efficiency of computing and maintain the tradeoff between CE and sensing performance.

Deep learning-based automatic modulation recognition (AMR) is essential for enabling access authorization and spectrum management for interconnected devices and sensors in Internet of Things (IoT) systems. However, the open collection of data and the use of third-party training resources may introduce security vulnerabilities, especially backdoor attacks. Current research allows attackers to mislead receivers into misclassifying signals as a specific modulation type, but the fixed position of the trigger restricts its use in complex wireless networks. In this work, we propose a novel spatially distributed multi-target backdoor attack (SMBA) method. This method utilizes a trigger pattern to manipulate all types of input signals into multiple target modulation types by embedding the stealthy trigger at different spatial locations within the input signals. SMBA disseminates malicious samples across multiple target types specified by attackers, making it difficult for defenders to predict the target type into which the modulation signal embedded with the trigger is classified. This work reveals new security threats to AMR and provides important insights for developing defense technologies for IoT systems.

In this paper, we propose a novel covert transmission scheme for reconfigurable holographic surface (RHS)-aided integrated sensing and communication (ISAC) system with imperfect channel state information (CSI). Considering full and partial channel uncertainty models, we jointly devise the digital and holographic beamforming along with the receive filter to maximize the worst-case and outage-constrained achievable rate (AR) of communication users while guaranteeing the sensing capability and covertness requirement. The resulting optimization problems are difficult to solve owing to the non-convexity caused by the semi-infinite constraints (SICs) and the coupled design variables. After approximating the worst-case and outage constraints by exploiting the S-procedure, successive convex approximation (SCA) and Bernstein-type inequality, we propose a secure solution that efficiently optimizes all variables by using convex optimization methods. To understand the proposed algorithm better, both the convergence and computational complexity are discussed. Simulation results show that by incorporating the RHS technique into the optimization design, the covert transmission performance of ISAC systems are improved while ensuring a certain level of sensing performance.

Large-scale multiple-input-multiple-output (MIMO) holds great promise for the fifth-generation (5G) and future communication systems. For near-field scenarios, the spherical wavefront model is commonly utilized to depict the propagation characteristics of large-scale MIMO communication channels. However, employing this modeling method necessitates the computation of angle and distance parameters for each antenna element, resulting in challenges regarding computational complexity. To solve this problem, we introduce a subarray decomposition scheme with the purpose of dividing the whole large-scale antenna array into several smaller subarrays. This scheme is implemented in the near-field channel modeling for large-scale MIMO communications between the base station (BS) and mobile receiver (MR). Essential channel propagation statistics, such as spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (CFs), and channel capacities, are derived and discussed. A comprehensive analysis is conducted to investigate the influences of the height of the BS, motion characteristics of the MR, and antenna configurations on the channel statistics. The proposed channel model criterions, such as the modeling precision and computational complexity, are also theoretically compared. Numerical results demonstrate the effectiveness of the presented communication model in obtaining a good tradeoff between modeling precision and computational complexity.

In this article, the Bayesian estimator and detector are proposed in the scenario of massive communication. The ultra massive multiple-input-multiple-output (MIMO) is established at the base station (BS), which is communicated with a huge number of online devices in the near field. In order to estimate the uplink channel responses, the novel nonorthogonal pilot sequences are designed and the principle of turbo decoding is applied. Then, the sparse estimation of extra large-scale channel state information (CSI) is performed depending on the extrinsic information transferring in the spatial domain and angular domain. Besides, the mixed analog-to-digital converter (ADC) architecture is considered to accomplish the linear and nonlinear measurements. Based on this framework, the tradeoff between the system performance and hardware overhead can be achieved. Additionally, a submodule-based segmentation technique is addressed to eliminate the energy spreading phenomenon caused by the near filed effects of the ultra massive MIMO. Specifically, we also analyze the theoretical statistical result of sparse channel estimation and device activity detection using the state evolution method. Furthermore, several engineering implementation strategies are provided to enhance the efficiency improvements in the practical system of massive communication. Numerical simulation results demonstrate that the satisfactory performance of estimation/detection is beyond other methods in terms of hardware costs and computational complexity in the extra large Internet of Things (IoT) network.

The Internet of Nanothings (IoNT) enables in-body communication, but transmitting signals to external devices remains a key challenge. Neural communication provides a promising interface, yet existing models often oversimplify membrane potentials as binary states, ignoring their subthreshold oscillatory dynamics. To address this, we propose a biologically realistic neural communication model that incorporates the resonate-and-fire (RF) neuron model, capturing the damped oscillations in membrane potential. Accordingly, we design two coding and modulation schemes: enhanced dual-pulse on-off keying (EDP-OOK), which aligns pulse intervals with the neuron’s oscillatory period for optimal excitation or suppression, and tunable dual-pulse on-off keying (TDP-OOK), which flexibly adjusts pulse intensity for energy-efficient suppression. The transmission efficiency is evaluated using the bit error rate (BER). Simulation results show the proposed schemes achieve reliable transmission with lower power consumption compared to conventional methods. This research opens up possibilities for efficiently connecting IoNT to external networks.

This paper investigates unmanned aerial vehicle (UAV) and an active reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) dual-function radar-communication (DFRC) system. The DFRC base station (BS) employs a hybrid analog-digital (HAD) architecture. Under the constraints of the active RIS and BS power budgets, the unit-modulus analog precoder, and the desired radar beamforming pattern, we jointly optimize the BS hybrid beamforming (HBF) and active RIS beamforming to maximize the signal-to-interference-plus-noise ratio (SINR) of user. Considering the non-convex SINR objective function and unit-modulus constraints, we propose a weighted minimum mean square error (WMMSE) method based on the penalty dual decomposition (PDD) framework and alternating optimization (AO). For the active RIS beamforming design, we introduce the semidefinite relaxation (SDR) method and a majorization-minimization (MM) method. Finally, the simulation results demonstrate the potential of active RIS in DFRC systems compared to the passive RIS.

Radio frequency fingerprint identification (RFFI) has become a crucial technology in physical layer authentication, and plays an important role in authenticating the identities of wireless communication devices in the Internet of Things (IoT). Although open-set recognition has been applied in RFFI tasks, these schemes still demand extensive RF signal samples. In this paper, few-shot open-set recognition is being dedicated to exploring in RFFI tasks. To surmount mentioned challenges, we propose meta-learning by gaussian prototype network (MLGPN) scheme to achieve the goal of few-shot open-set recognition. MLGPN adopts the Mahalanobis distance between the embedding feature and the gaussian prototype as its metric. With the introduction of open-set loss function, the proposed scheme shows excellent open-set recognition performance. It is worth mentioning that meta-learning not only satisfies the demands of few-shot scenarios, but also enables new devices to join and leave without the need for retraining. Experiments conducted based on real LoRa RF signals confirmed the excellent performance of our proposed scheme for few-shot open-set recognition which surpasses traditional prototypical network model by 5.3% of AUC and 6.7% of ACC under the 1-shot condition. Compared with other schemes, the proposed scheme also demonstrated significant advantages.

In this paper, we study the robust resource optimization for the intelligent reflecting surface (IRS)-assisted full duplex (FD) integrated communication, computing and sensing (ICCS) systems. The base station (BS) serves uplink computing users, downlink communication users and sense targets simultaneously, while the imperfect channel state information (CSI) from IRS to users are considered. Next, we formulate an optimization problem of maximizing sum rate by jointly designing the BS transmit/receive beamforming, IRS reflection coefficients, computing user transmit power and local computing resources. Due to its difficulty to directly solve the formulated problem, we divide it into several subproblems and propose an alternative iterative optimization algorithm. Then, by applying the S-Procedure, singular value decomposition (SVD) and successive convex approximation techniques, each subproblem is transformed into the semidefinite programming (SDP), and the semidefinite relaxation (SDR) is applied to obtain the solution by dropping the rank-one constraint. The final solutions are obtained by alternatively solving all subproblems until convergence. Finally, the simulation results show the performance of the proposed scheme outperforms that of baseline schemes.

A radio environment map (REM) is a visualization method that display electromagnetic properties, such as received signal strength, channel gain, and power spectrum density in combination with geographic information. The map can effectively support modern Internet of Things (IoT) network planning and resource management. A novel REM construction scheme of an arbitrary height and frequency in 3-D space is studied in this article. First, a complex urban environment is considered, where the radiation sources transmit wireless signals in different frequency bands. Then, the construction is sliced into 2-D planes with various elevations to achieve precise and efficient sensing of 3-D space. For near-ground scenarios, preliminary global interpolation based on linear unbiased estimation is first performed to obtain a coarse REM, and then graph neural networks are utilized to further extract the relationships and features of the spatial nodes to improve the construction accuracy. For high-altitude scenarios, a small range of interpolation is carried out on the basis of linear unbiased estimation with the clustering center obtained by clustering the known sensing nodes as the center of the circle. Then the global construction is implemented via domain transformation processing to increase the construction speed. Finally, the 2-D REMs are stacked in sheets according to elevation to form a 3-D REM. The simulation results demonstrate the effectiveness and superiority of the proposed scheme.

To guarantee the low-congestion performance and quality of service (QoS) requirements of multi-services in Mega Low Earth Orbit Constellation Networks (MLEOCN), this paper focuses on the comprehensive communication link model in MLEOCN, commencing from users to access satellites, relayed by relay satellites, and finally delivered to the gateway by feeder satellites. Aiming at the problems of high congestion and low throughput in traditional path planning algorithms, we innovatively propose a multi-objective optimization service-correlated path optimization algorithm based on stochastic hill climbing strategy (MSCPO-SHCS). The algorithm initially achieves the joint optimization of three metrics through regular mapping and judicious weighting. Subsequently, it assesses the interplane hop via geometric parameter theory analysis (GPTA), then decouples the large-scale mixed integer optimization problem into the integer optimization problem superimposed linear programming problem, and ultimately employs the stochastic hill climbing strategy (SHCS) for path intelligent optimization. Based on the path Gaussianity assumption, we theoretically prove and numerically verify the convergence of the proposed algorithm. The simulation results indicate that the proposed algorithm boosts the throughput and load balancing coefficient compared with the greedy strategy, service-uncorrelated, minimum hop count, and resource allocation optimization. Additionally, it decreases the hop count compared with the maximum throughput and maximum balancing coefficient and maintains the optimal overall performance.

Existing works mainly rely on the far-field planar-wave-based channel model to assess the performance of reconfigurable intelligent surface (RIS)-enabled wireless communication systems. However, when the transmitter and receiver are in near-field ranges, the investigation of the channel statistics based on the planar-wave-based model will result in relatively low computing accuracy. To tackle this challenge, we initially develop an analytical framework for sub-array partitioning. This framework divides the large-scale RIS array into multiple sub-arrays, effectively reducing modeling complexity while maintaining acceptable accuracy. Then, we develop a beam domain channel model based on the proposed sub-array partition framework for large-scale RIS-enabled unmanned aerial vehicle (UAV)-to-vehicle communication systems, which can be used to efficiently capture the sparse features of RIS-enabled UAV-to-vehicle channels in both near-field and far-field ranges. Furthermore, some important propagation characteristics of the proposed channel model, including the spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (FCFs), channel capacities, and path loss statistics with respect to the different physical features of the RIS array and non-stationary properties of the channel model are derived and analyzed. Finally, simulation results are provided to demonstrate that the proposed framework is helpful to achieve a good tradeoff between the modeling complexity and accuracy for investigating the channel propagation characteristics, and therefore providing highly-efficient communications in RIS-enabled air-ground wireless networks.

The Internet of Nan-othings (IoNTs) have been extensively explored as potential communication technologies for in-body applications. The transmission of information from within the body to the external environment has become a popular and pressing issue that needs to be addressed. Neural communication has been proposed as a promising method, utilizing an action potential (AP), transient changes in membrane potential, as a fundamental unit for communication. Current research conceptualizes the membrane potential into two states: an excited state that generates an AP upon stimulation and a resting state absent of stimulation. However, this assumption overlooks the inherent oscillatory characteristics of membrane potential, which exhibit a discernible sensitivity to specific input frequencies. In this paper, we incorporate the oscillations characterized by the Izhikevich model into the channel model. Here, APs are generated when the inter-spike interval closely aligns with, or is a multiple of, the oscillatory period. Following this, we introduce an adaptive coding and modulation strategy. To represent “1”, a pair of pulses, separated by one period, are used to stimulate an AP. In contrast, to represent”0”, two consecutive pulses, distanced by half a period, are employed to inhibit the membrane potential. The transmission efficiency is evaluated by bit error rate (BER) and mutual information (MI). Numerical simulation results demonstrate that the proposed neural communication system is biologically plausible and exhibits higher resistance to interference. This research opens up possibilities for connecting IoNTs to external networks.

An arbitrary-elevation two-sphere reference model is utilized to mimic the unmanned aerial vehicle (UAV) air-to-air fading channels. The model considers the line-of-sight (LoS), the single-bounced transmit (SBT), the single-bounced receive (SBR), and the double-bounced (DB) rays. Based on this model, the closed-form expression of the space-time correlation function (ST-CF) is obtained for the first time under the widely-used assumption of von Mises-Fisher (vMF) distributed scatterers. To further improve the model’s adaptability to realistic scattering environments, the distribution of the scatterers is generalized from a single unimodal vMF density into a mixture that can possess multimodality. Using the single-vMF ST-CF, the ST-CF under the mixture is also written in closed-form. Corresponding to the reference channel model, both the deterministic and the stochastic simulation models are provided, which yield consistent results with the respective derived expressions. This validates the correctness of the suggested closed-form ST-CFs. Moreover, a detailed analysis of the outage probability and the outage capacity is reported, which offers revealing insights into the behaviors of the system performance with respect to change of some key model parameters under multimodal distributions of the scatterers.

A 3-D arbitrary-elevation two-cylinder reference model is proposed for multiple-input-multiple-output (MIMO) air-to-air communications in unmanned aerial vehicle (UAV) channels. This model accounts for not only the Line-of-Sight (LoS) but also the single-bounced at the transmitter (SBT) and the single-bounced at the receiver (SBR), as well as the double-bounced (DB) rays. Therefore, it is endowed with a high adaptability to various UAV-to-UAV communication scenarios. From the reference model, a closed-form expression of the space-time correlation function (ST-CF) is derived. This expression is shown to be the generalizations of many existing correlation functions from the 2-D one-ring, the 3-D low-elevation one-cylinder, the 2-D two-ring, and the 3-D low-elevation two-cylinder model. Corresponding deterministic and stochastic simulation models are also developed in addition to the reference model. The well agreements between the channel capacities obtained from the simulation models and those from the derived ST-CF not only display the usefulness of the simulators but also confirm the correctness of the derivations. Based on the derived closed-form ST-CF, the effects of some model parameters on the capacity are evaluated in a computationally efficient manner.

Millimeter-wave (mmWave) communication using lens antenna array has emerged as a promising techique for the next generation wireless communication systems, which has the advantage of considerably reducing the number of required radio-frequency (RF) chains. However, the beamspace channel estimation is a challenging problem due to the fact that the number of RF chains is much smaller than that of transmit/receive antennas at the base station (BS). In this paper, motivated by the sparsity structure of lens-antenna based beamspace channels, we first model beamspace channel estimation problem as a sparse signal recovery problem, which can be solved by the compressed sensing (CS) algorithms. Then, we propose a simplified learned approximate message passing (SL-AMP) network by pre-learning the prior parameters of beamspace channel. Specifically, we use the Wasserstein generative adversarial net (GAN) to learn the underlying channel distribution and generate the corresponding samples to address the lack of training data problem. Next we choose the Bernoulli Gaussian mixture distribution to capture the channel prior and derive the corresponding shrinkage function. Finally, simulations results show that the proposed SL-AMP network can achieve relatively better performance with much lower training complexity compared with the existing LAMP network.

In this paper, a three-dimensional (3D) geometry-based stochastic model (GBSM) empowered by reconfigurable intelligent surface (RIS) is presented for multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) communication systems. Owing to the channel non-stationarity, spherical wavefront, and antenna configurations in RIS-empowered V2V channel, the geometry-based channel models suffer from high computational complexity, thereby leading to high hardware burden. To address this issue, a novel beam domain channel model (BDCM) is generated from the proposed geometry-based channel model through a beamforming operation based on discrete Fourier transform (DFT). To describe the non-stationarities of the V2V channels empowered by RIS, the channel model presented in this paper introduces real-time velocities and accelerations to capture the motion features of the communication terminals. The propagation characteristics including spatial cross-correlation functions (CCFs), temporal autocorrelation functions (ACFs), frequency correlation functions (FCFs), and channel capacities of the proposed communication system are derived and discussed. Some comparisons between the propagation characteristics of the proposed GBSM and those based on BDCM with respect to the different physical parameters of RIS and different environmental variables are investigated. Furthermore, numerical results indicate that the proposed channel model works well by changing the velocity parameters in different motion states.

In this paper, we consider the channel modeling of a heterogeneous vehicular integrated sensing and communication (ISAC) system, where a dual-functional multi-antenna base station (BS) intends to communicate with a multi-antenna vehicular receiver (MR) and sense the surrounding environments simultaneously. The time-varying complex channel impulse responses (CIRs) of the sensing and communication channels are derived, respectively, in which the sensing and communication channels are correlated with shared clusters. The proposed models show great generality for the capability in covering both monostatic and bistatic sensing scenarios, and as well for considering both static clusters/targets and mobile clusters/targets. Important channel statistical characteristics, including time-varying spatial cross-correlation function (CCF) and temporal auto-correlation function (ACF), are derived and analyzed. Numerically results are provided to show the propagation characteristics of the proposed ISAC channel model. Finally, the proposed model is validated via the agreement between theoretical and simulated as well as measurement results.

In this paper, we propose a three-dimensional (3D) physics-based double reconfigurable intelligent surface (RIS) cooperatively assisted multiple-input multiple-output (MIMO) stochastic channel model for unmanned aerial vehicle (UAV)-to-ground communication scenarios. The double-RIS is distributed on the surface of buildings can assist the UAV transmitter to reflect its own signals to the ground receiver (GR), and simultaneously enhance the propagation by passive beamforming on the RISs. In the proposed channel model, we derive thepath power gainsfor different propagation links between the UAV and GR, thus enabling the proposed channel model to effectively characterize both large- and small-scale fading characteristics of realistic UAV-to-ground communication systems. Furthermore, we derive the critical propagation properties of the proposed channel model, including the spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency correlation functions (FCFs), with respect to different RIS reflection phase configurations as well as different RIS orientation angles. Numerical simulation results demonstrate the propagation characteristics of the proposed double-RIS assisted UAV-to-ground channel model behave better than those of traditional channel models with single-RIS link or LoS link, thereby validating the necessity of introducing double-RIS into UAV-to-ground communication.

This paper studies the reconfigurable intelligent surface (RIS)-enhanced channel estimation and device activity detection technique for the next generation massive internet of things (IoT) networks. Thanks to its low cost, RIS can be introduced into massive IoT networks to extend the area coverage and support more online devices. However, introducing RIS also brings new challenges in channel estimation and device detection for massive connectivity systems owning to the resulting cascaded channel and its inherent passive characteristics. To address this issue, we first formulate the RIS-aided channel estimation and device activity detection as a joint sparse signal recovery problem by simultaneously exploring the sparsity of sporadic transmission and RIS-aided channel links. After that, an RIS-aided generalized Turbo multiple measurement vector algorithm to estimate the channels between the devices and the base station, and detect the active devices jointly under different channel distributions, i.e., the Bernoulli Gaussian scale mixture distribution and the Bernoulli Gaussian approximation distribution. Furthermore, we analyze the state evolution equations of the proposed channel estimation technique and the theoretical detection results from the perspective of missing detection and false alarm probabilities are also provided. Numerical results confirm the correctness of the theoretical analysis, and show that RIS is beneficial for improving the mean square error performance of the channel estimators, as well as the active device detection performance of detectors in massive connectivity systems.

Reconfigurable intelligent surface (RIS)-assisted communications has been a hot topic due to its promising advantages for future wireless networks. Existing works on RIS-assisted channel modeling have mainly focused on far-field propagation condition with planar wavefront assumption. In essence, the far-field condition does not always hold because the RIS array dimension may be comparable to the terminal distance, especially in RIS-assisted mobile networks. To this end, we propose a hybrid far- and near-field stochastic channel model for characterizing a RIS-assisted vehicle-to-vehicle (V2V) propagation environment, which takes into account both far-field and near-field propagation conditions. To achieve the balance between the modeling accuracy and complexity for the investigation of the RIS-assisted V2V propagation characteristics, we develop a sub-array partitioning scheme to dynamically divide the entire RIS array into several smaller sub-arrays, which makes planar wavefront assumption applicable for the sub-arrays. Important channel statistical properties, including spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency correlation functions (FCFs), are derived and investigated. Simulation results are provided to show the performance of the proposed sub-array partition based hybrid far- and near-field modeling solution for RIS-assisted V2V channels.

Abstract This paper studies unmanned aerial vehicles (UAV) enabled industrial Internet of Things while a UAV dispatched to collect data of low‐power ground sensor nodes (SNs) in multi‐obstacle environment. The authors aim to minimize the completion time while satisfying the communication link constraints of each SN and obstacle avoidance, data collection requirements etc. To this end, the authors first formulate the completion time minimization problem by jointly optimizing the UAV trajectory and collection sequence of SNs. The problem is difficult to be optimally solved, as it is non‐convex. To tackle this problem, the authors first transform the original problem to a Traveling Salesman Problem‐like (TSP‐like) problem based on a hover point that can naturally satisfy the communication link constraints of data collection. The dynamic programming (DP) algorithm to figure out the order in which the UAV collects each SN, which gives the initial path of the UAV traversing each SN from the beginning point to the end point. Next, the authors consider the general scenarios of data collection tasks where the UAV also communicates while flying. The authors construct an equivalent problem with integer variable constraints for the original problem with indicative function constraints. The authors rewrite the non‐convex constraints of the equal problem by introducing slack variables and leveraging the SCA, and add the discrete region threat constraints for the traditional path discretization method. Finally, the simulation results verify the effectiveness of the proposed algorithm under different parameter configurations.

Channel estimation is one of the main challenges for implementing reconfigurable intelligent surface (RIS)-aided communication system with a large number of antennas at the base station (BS) because RIS is equipped with many passive reflective elements without active transmitting/receiving and signal processing abilities. In this paper, we focus on the channel estimation in a general RIS-aided multi-user mmWave communication system. Specifically, a novel two-phase channel estimation scheme consisting of on-line and off-line phases is proposed to estimate the BS-RIS channel, RIS-user channel, and BS-user channel, respectively. Inspired by the characteristics of few scatterers in mmWave communication systems, the antenna domain channels are converted into the virtual angular domain channels by using the discrete Fourier transform (DFT) matrix. Thus, the estimation problem can be formulated as a compressed sensing (CS) problem, and solved by using efficient vector approximate message passing (VAMP) algorithm with expectation-maximization (EM) to learn unknown parameters and obtain the estimates simultaneously. Simulation results show that, the above algorithm with two choices of prior distributions in the proposed mmWave RIS-aided multi-user system has fast convergence speed and desirable estimation performance.

Reconfigurable intelligent surface (RIS) consisting of a large number of programmable near-passive units has been a hot topic in wireless communications due to its capability in providing smart radio environments to enhance the communication performance. However, the existing research are mainly based on simplistic channel models, which will, in principle, lead to inaccurate analysis of the system performance. In this paper, we propose a general three-dimensional (3D) wideband non-stationary end-to-end channel model for RIS assisted multiple-input multiple-output (MIMO) communications, which takes into account the physical properties of RIS, such as unit numbers, unit sizes, array orientations and array configurations. By modeling the RIS by a virtual cluster, we describe the end-to-end channel by a superposition of virtual line-of-sight (V-LoS), single-bounced non-LoS (SB-NLoS), and double-bounced NLoS (DB-NLoS) components. We also derive an equivalent cascaded channel model and show the equivalence between end-to-end and cascaded modeling of RIS channels. Then, a sub-optimal solution with low complexity is used to derive the RIS reflection phases. The impact of physical properties of RIS, such as unit numbers, unit sizes, array orientations, array configurations and array relative locations, on channel statistical characteristics has been investigated and analyzed, the results demonstrate that the proposed model is helpful for characterizing the RIS-assisted communication channels.

Unmanned aerial vehicle (UAV) communications exploiting millimeter wave (mmWave) can satisfy the increasing data rate demands for future wireless networks owing to the line-of-sight (LoS) dominated transmission and flexibility. In reality, the LoS link can be easily and severely blocked due to poor propagation environments such as tall buildings or trees. To this end, we introduce a reconfigurable intelligent surface (RIS), which passively reflects signals with programmable reflection coefficients, between the transceivers to enhance the communication quality. Specifically, in this paper we generalize a three-dimensional (3D) non-stationary wideband end-to-end channel model for RIS auxiliary UAV-to-ground mmWave multiple-input multiple-output (MIMO) communication systems. By modeling the RIS as a virtual cluster, we study thepower delivering capabilityof RIS as well as thefading characteristicof the proposed channel model. Important channel statistical properties are derived and thoroughly investigated, and the impact of RIS reflection phase configurations on these statistical properties is studied, which provides guidelines for the practical system design. The agreement between theoretical and simulated as well as measurement results validate the effectiveness of the proposed channel model.

Abstract This study proposes an approximate algorithm for the arbitrary angle of arrival (AoA) incident power spectrum in wireless environments. The approach expands approximate algorithms for small angles to the large‐angle AoA PDF. To achieve this, the fading correlation of wireless channels is calculated and derived for various theoretical fittings and measured data. Then, the multiple‐input multiple‐output (MIMO) channel model is reconstructed for special environments using spatial fading correlation (SFC). Following this, the approximate algorithm and its complexity are investigated in depth in the SFC of multi‐antenna arrays with small AoA angles under uniform, Gaussian, and Laplacian distributions, which can function in macrocell and microcell environments. Further, the closed form formulas for SFC are derived for the small angle spread of incident signals. The formulas are extended to simulate these cases with large angle spreads, which result in an increasing calculation complexity, particularly for the actual measured results. The selection of the number of samples and the weighting coefficient in the large angle spreads and its fitting accuracy are considered in detail. Based on this relationship, the properties of the temporal correlation and channel of the MIMO uniform linear array are studied and discussed.

Abstract Intelligent reflecting surface (IRS) is a new concept originating from metamaterials, which can achieve beamforming through controllable passive reflecting. This device makes it possible to engineer the wireless communication environment, and has drawn increasing attention. However, the associated channel models in current literature are mainly borrowed from conventional wireless channel models directly, omitting the unique features of IRS. In this paper, a geometry‐based stochastic channel model for IRS‐assisted wireless communication system is employed. The model has certain accuracy and low computational complexity. In particular, it captures the correlations of subchannels associated with different IRS elements, which is typically not considered in current works. Based on this channel model and the derived channel spatial correlation functions (CFs), an iterative reflection coefficients configuration method is proposed exploiting statistical channel state information to maximise the ergodic channel capacity. The impacts of the IRS spatial positions as well as the number of the IRS elements on the ergodic channel capacity is investigated through simulations. It is found that to obtain a larger ergodic channel capacity, the IRS should be placed in the vicinity of either the transmitter side or the receiver side, which is a useful guideline for practical deployment.

Intelligent reflecting surface (IRS), which is composed of a large number of low-cost passive elements, has the ability to reflect the incident signal independently with an adjustable phase and amplitude shifts, has been regarded as a key and emerging technology for achieving the cost-effectively spectrum and addressing energy issues in the next generation of wireless networks. In this paper, we propose a general wideband non-stationary channel model for IRS-assisted multiple-input multiple-output (MIMO) communication scenarios, which aims at capturing the underlying propagation characteristics of IRS-assisted communication systems. By properly adjusting the key system parameters, the proposed channel model can be used to describe various IRS-assisted communication scenarios. Furthermore, we separate the channel between the mobile transmitter (MT) and mobile receiver (MR) into the subchannel between the MT and IRS, the subchannel between the IRS and MR, and the subchannel between the MT and MR. The physical properties of each subchannel are investigated accordingly; then, we develop an equivalent channel model to study the key characteristics of the proposed IRS-assisted channel model, such as the time-varying spatial-temporal (ST) cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency CCFs. Finally, numerical results demonstrate that the proposed channel model is practical for describing the IRS-assisted MIMO wireless communication scenarios.

For efficiently investigating the statistical properties of wideband multiple-input multiple-output (MIMO) channels for vehicle-to-vehicle (V2V) communication scenarios, we propose a novel computationally efficient solution to estimate the parameters of the proposed channel model for different propagation delays in this paper. To be specific, we first introduce a Unitary transformation method to estimate the propagation delay of the proposed channel model for the first tap in the preliminary stage before the mobile transmitter (MT) and mobile receiver (MR) move. Then, we estimate the real-time angular parameters based on the estimated delay and moving time/directions/velocities of the MT and MR. Furthermore, we estimate the expressions of the real-time complex channel impulse responses (CIRs), which can be used to characterize the physical properties of the proposed channel model, by substituting the estimates of the time-varying AoD and AoA and model parameters into the complex CIRs. Numerical results of the channel characteristics fit the theory results very well, which validate that the proposed channel model is practical for characterizing the beyond fifth-generation (B5G) V2V communication systems.

Considering mobile terminals with time-varying velocities and randomly moving clusters, a novel multi-mobility non-stationary wideband multiple-input multiple-output (MIMO) channel model for future intelligent vehicle-to-everything (V2X) communications is proposed. To describe the non-stationarity of multi-mobility V2X channels, the proposed model employs a time-varying acceleration model and a random walk process to describe the motion of the communication terminals and that of the scattering clusters, respectively. The evolution of the model parameters over time and the stochastic characteristics of the phase shift caused by the time-varying Doppler frequency are derived. The proposed model is sufficiently general and suitable for characterizing various V2X communication scenarios. Under two-dimensional (2D) non-isotropic scattering scenarios, the important channel statistical properties of the proposed model are derived and thoroughly investigated. The impact of the random walk process of the clusters and the velocity variations of the communication terminals on these statistical properties is studied. The simulation results verify that the proposed model is useful for characterizing V2X channels.

Connected devices generate large amount of data for IoT applicatons. Assisted by edge computing, caching IoT data at the edge nodes is considered as a promising technique for its advantage in reducing network traffic and service delay of cloud platform. However, the IoT data is characterized by transient lifetime and cache capacity that is limited by the edge nodes. As a consequence, caching policy should consider both data transiency and storage capacity of edge nodes. Inspired by the success of deep reinforcement learning (DRL) in deal with Markov Decision Process (MDP) problem in unknown environment, A DRL-based algorithm for edge caching problem is proposed in this paper. The proposed Advantage Actor Critic (A2C)-based algorithm is aimed at maximizing the long-term energy saving without knowledge of the IoT data popularity profiles. Simulation results demonstrate that the proposed DRL-based algorithm can achieve higher energy saving and cache hit ratio compared with the baseline algorithms.

This study presents a multiple‐input multiple‐output (MIMO) statistical channel model for vehicle‐to‐vehicle (V2V) line‐of‐sight (LOS) and non‐LOS mobile communication system in a rectangular tunnel environment. In order to improve the V2V communication system performance, the authors introduced the elevation and azimuth angles in three‐dimensional modelling based on the original literature. In the proposed reference channel model, the single‐bounced scattering propagation path of electromagnetic signals is considered and it is assumed that the scatterers are randomly distributed on the rectangular tunnel wall. Starting from the statistical physical channel model, the time‐variant transfer functions are derived. Thereafter, the analytical expressions of the space–time–frequency cross‐correlation function, the space cross‐correlation function, the temporal autocorrelation function, the frequency correlation function, and MIMO channel capacity are derived. The influence of model parameters on the performance of the V2V communication system is also analysed. This work provides an innovative approach to channel modelling in a rectangular tunnel mobile communication environment.

In this article, a wideband Direction-of-Arrival (DOA) estimation method for underdetermined scenarios is proposed, which effectively solves the basis mismatch problem. Based on the fractional Fourier transform (FRFT), the wideband received signal model with a coprime array is first derived by exploiting the aggregation characteristic of wideband linear frequency modulated (LFM) signals in the fractional Fourier (FRF) domain. Then, in order to increase the degree of freedom, an extended uniform linear array is built, and the covariance matrix of the signal is reconstructed by employing the penalized atomic norm minimization with the consecutive spatial dictionary. Meanwhile, without the knowledge of the noise level, the noise variance is estimated from the noisy incomplete data, which is utilized to improve the covariance matrix reconstruction performance. Additionally, for the unconditional model, the Cramér-Rao bound for the wideband DOA estimation based on a coprime array is derived. Different from the existing methods, the proposed method not only can estimate more DOAs of wideband signals than the number of physical sensors in the presence of unknown amplitude distortion but also can obtain more accurate DOA estimation performance without basis mismatch. The effectiveness of the proposed method is verified by our numerical results.

In this article, we propose a three-dimensional (3D) multi-input multi-output (MIMO) channel model for air-to-ground (A2G) communications in unmanned aerial vehicles (UAV) environments, where the UAV transmitter and ground receiver are in motion in the air and on the ground, respectively. A novel angular estimation algorithm is proposed to estimate the real-time azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) based on the non-stationary nature of the channel model. In the model, we investigate the time-varying spatial cross-correlation functions (CCFs) and temporal auto-correlation functions (ACFs) with respect to the different moving directions and velocities of the UAV transmitter and ground receiver. Furthermore, we derive and study the Doppler power spectral densities (PSDs) and power delay profiles (PDPs) of the proposed channel model. Numerical results show that characteristics of the proposed channel model are very close to those of practical measurements, which provide a new and practical approach to evaluate the performance of next generation UAV-MIMO communication systems.

With the rapid development of cloud computing, the user-centric networks with the baseband unit pool have attracted a great deal of attentions in academic and industrial fields. However, limited fronthaul capacity and computation resource have become the bottlenecks inevitably. Thus, this paper investigates the power-efficient transmission in user-centric networks by considering both fronthaul capacity and computation resource constraints, where multiple access points (APs) and user equipments (UEs) are distributed. Specifically, a joint optimization of the beamforming vectors, AP-UE association strategy and transmission time is proposed to minimize the total power consumption (TPC). The formulated mixed integer non-linear problem (MINLP) is NP-hard. To address this problem, the MINLP is first transformed into a convex one via the successive convex approximation and semidefinite relaxation methods. Then, an iterative but effective algorithm is designed by using the property of the solution and applying the Lagrangian dual method. Simulation results show that the proposed algorithm converges rapidly and outperforms benchmark algorithms in terms of TPC.

In this paper, we present a three-dimensional (3D) wideband geometry-based channel model for multiple-input and multiple-output (MIMO) vehicle-to-vehicle (V2V) Ricean fading channels, where the received signal is constructed as a sum of line-of-sight (LoS) and non-LoS (NLoS) propagation rays. We first introduce multiple confocal semi-ellipsoid models to depict roadside environments, which is able to efficiently model scatterers with identical delays on the same semi-ellipsoid. Therefore, the V2V channel characteristics for different propagation delays can be investigated. Moreover, the proposed models can easily be reduced to various simplified channel models by properly adjusting model parameters. Using this channel model, the channel characteristics, i.e., the spatial correlation functions (CFs) and Doppler power spectral densities (PSDs), are investigated. The numerical results are very close to the previous results and measurements, thereby demonstrating the accuracy of the proposed channel model.

This paper presents 3-D vehicle massive multiple-input multiple-output (MIMO) antenna array model for vehicle-to-vehicle (V2V) communication environments. A spherical wavefront is assumed in the proposed model instead of the plane wavefront assumption used in the conventional MIMO channel model. Using the proposed V2V channel model, we first derive the closed-form expressions for the joint and marginal probability density functions of the angle of departure at the transmitter and angle of arrival at the receiver in the azimuth and elevation planes. We additionally analyze the time and frequency cross-correlation functions for different propagation paths. In the proposed model, we derive the expression of the Doppler spectrum due to the relative motion between the mobile transmitter and mobile receiver. The results show that the proposed 3-D channel model is in close agreement with previously reported results, thereby validating the generalization of the proposed model.

In this paper, we present a three-dimensional (3D) geometric channel model for vehicle-to-vehicle (V2V) communication environments. We adopt a two-cylinder model to describe moving vehicles as well as semi-ellipsoid model to depict stationary roadside scenarios, where the received signal is constructed as a sum of the line-of-sight (LoS), single-, and double-bounced rays. Accordingly, the proposed channel model is sufficient for depicting a variety of V2V scenarios, such as macro-, micro-, and picocells. From the reference model, we analyze the proposed channel statistics such as space correlation functions (CFs) and frequency CFs are studied for different channel parameters. Additionally, we perform the Doppler frequency due to the relative motion between the mobile transmitter (MT) and mobile receiver (MR). The results demonstrate that the proposed model can fit those of the previous channel models and the measurement results for V2V scenarios very well, which validate the accuracy of the proposed 3D channel model.

The development of realistic channel models that can efficiently and accurately describe a wireless propagation channel is a key research area. In this study, a generalised three‐dimensional (3D) scattering channel model for land mobile systems is proposed to simultaneously describe the angular arrival of multi‐path signals in the azimuth and elevation planes. The model considers a base station located at the centre of a 3D semi‐spheroid‐shaped scattering region and a mobile station (MS) located within the region. Using this channel model, the authors first derive the closed‐form expression for the joint and marginal probability density functions of the angle of arrival and time of arrival measured at the MS corresponding to the azimuth and elevation angles. Next, they derive an expression for the Doppler spectra distribution due to the motion of the MSs. Furthermore, they analyse the performance of multiple‐input and multiple‐output antenna systems and their numerical results. The results show that the proposed 3D scattering channel model performs better compared with previously proposed 2D models for outdoor and indoor environments. They compare the results with previous scattering channel models and measurement results to validate the generalisation of their model.