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.

Unmanned aerial vehicles (UAVs) are emerging as a promising platform for enabling integrated sensing and communication (ISAC) in multi-cell systems due to their deployment flexibility. However, this flexibility also introduces significant challenges, particularly co-channel interference at the UAV receiver. In this paper, we propose a novel adaptive co-channel interference mitigation model for UAV-enabled multi-cell systems. Specifically, the proposed model consists of two key components: a cost function and an update algorithm. First, we derive a new cost function that incorporates both magnitude and phase errors–critical metrics for guiding the estimated signal toward the desired signal. Second, the cost function is extended to formulate a parameter update algorithm, whose effectiveness is analyzed using both geometric and entropy-based approaches. Simulation results demonstrate that the proposed method outperforms state-of-the-art techniques, establishing it as a robust solution for interference mitigation in UAV-enabled multi-cell ISAC systems.

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.

The advent of extremely large antenna arrays and high-frequency signaling is expected to enable next-generation integrated sensing and communication (ISAC) networks to predominantly operate in the near-field region. Due to the dual influence of distance and angle on wave propagation characteristics in the near-field region, accurately modeling these characteristics remains a critical challenge. Motivated by the potential of large language models (LLMs) in angle prediction and distance estimation, an LLM-enhanced multi-objective optimization problem (MOOP) is developed to accurately capture the dependence of the channel on both the angular position and distance. The formulated LLM-enhanced MOOP framework is decomposed into a series of sub-problems, which can balance spectral efficiency for communication and localization accuracy for sensing. To overcome the computational and energy challenges associated with LLMs, a gray wolf optimization (GWO)-based algorithm is integrated as black-box search operator with LLM-specific prompt engineering to solve these sub-problems. Numerical results demonstrate that the proposed LLM-GWO scheme achieves an trade-off between communication and sensing performance, outperforming baseline approaches in terms of both Pareto front quality and convergence.

Safety incidents caused by children trapped in vehicles are a serious global problem. Existing solutions for child presence detection (CPD) are limited by specialized hardware or detection delays that exceed safety standards. To address this problem, this study proposes an innovative system to detect the presence of a child trapped in a car using channel state information (CSI), analyzing the effect of motion on CSI in subcarrier dimension through modeling and introducing new metrics to quantify environmental changes. The system is implemented using a commercial Wi-Fi chipset and tested in a real vehicle environment using data collected from eight people of different ages. Experimental results show that we achieved 99.19% detection accuracy within a 1-second time window at a low sampling rate of 20 Hz. This result represents a significant advancement in detection latency for CPD systems and lays the groundwork for widespread adoption of CPD systems based on Wi-Fi devices.

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.

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.

Mobile edge computing (MEC) has emerged as a cutting-edge technique that brings computation and storage resources closer to the edge of the mobile network. However, MEC is vulnerable to be attacked by malicious users. To improve the security of computation tasks and enhance user connectivity, we design a deep reinforcement learning (DRL) network for reconfigurable intelligence surface (RIS)-aided MEC system. Specifically, we jointly optimize the phase shifts at the RIS, tasks offloaded by users and task assignment to maximize the secrecy offloading capacity and minimize energy consumption under different delay requirements of users. Furthermore, a multi-agent twin delayed deep deterministic policy gradient (TD3)-based algorithm is exploited to tackle the non-convex optimization problem. Numerical results validate the feasibility and applicability of our proposed scheme, demonstrating that the proposed scheme significantly improves the security and energy performance of the system compared to the baseline DRL algorithm.

Distributed reconfigurable intelligent surfaces (RISs) provide rich macro-diversity coverage due to different locations of the RISs, which is beneficial to combat coverage holes. However, the system performance relies on the effective coordination of multiple RISs. In particular, distributed RIS-assisted power allocation and the phase shifts of RISs should be jointly designed under nonlinear scheduling constraints. Thus, the resource allocation scheme for distributed RIS-assisted multiuser system is a crucial challenge. To tackle these issues, joint power allocation, phase shifts and communication scheduling design for distributed RIS-assisted systems is investigated in this paper, where all RISs simultaneously and cooperatively serve multiple users. To overcome the formulated nonconvex optimization problem, the original problem is decoupled into three subproblems and solved in an iterative manner. Specifically, we first consider the subproblem of power allocation, which can be solved via maximizing the ergodic achievable rate. By applying the ergodic rate, an approximate closed-form solution is formed for the power allocation. Subsequently, the phase shifts are optimized using the minimization-maximization optimization methods. Finally, a communication scheduling scheme is presented to address the scheduling variables. Numerical simulations are conducted to demonstrate that the considered solution outperforms the existing benchmark and achieves a near-optimal spectral efficiency.

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.

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.

Reconfigurable intelligent surfaces (RISs) have attracted extensive attention in millimeter wave (mmWave) systems because of the capability of configuring the wireless propagation environment. However, due to the existence of a RIS between the transmitter and receiver, a large number of channel coefficients need to be estimated, resulting in more pilot overhead. In this paper, we propose a joint sparse and low-rank based two-stage channel estimation scheme for RIS-assisted mmWave systems. Specifically, we first establish a low-rank approximation model against the noisy channel, fitting in with the precondition of the compressed sensing theory for perfect signal recovery. To overcome the difficulty of solving the low-rank problem, we propose a trace operator to replace the traditional nuclear norm operator, which can better approximate the rank of a matrix. Furthermore, by utilizing the sparse characteristics of the mmWave channel, sparse recovery is carried out to estimate the RIS-assisted channel in the second stage. Simulation results show that the proposed scheme achieves significant performance gain in terms of estimation accuracy compared to the benchmark schemes.

Reconfigurable intelligent surfaces (RISs) are deemed as a potential technique for the future of the Internet of Things (IoT) due to their capability of smartly reconfiguring the wireless propagation environment using a large number of low-cost passive elements. To benefit from RIS technology, the problem of RIS-assisted channel state information (CSI) acquisition needs to be carefully considered. Existing channel estimation methods usually ignored the different channel characteristics of direct channel and reflected channels. In fact, the reflected channel can be smartly configured by adjusting the phase shifts of the RIS, which is different from the direct channel due to the different path loss exponents between the transmitter and receiver. Therefore, it is necessary to further develop a RIS-assisted channel estimation to determine the direct and reflected channels, respectively. In this paper, we study a RIS-assisted channel estimation that jointly exploits the properties of the direct and the reflected channel to provide more accurate CSI. The direct channel is estimated using weighted$\ell_1$norm minimization, while the reflected channel is modeled based upon the robust$\ell_{1,\tau}$norm minimization to sequentially estimate the channel parameters. Moreover, by combining the gradient descent and the alternating minimization method, a flexible and fast algorithm is developed to provide a feasible solution. Simulation results demonstrate that an RIS-aided MIMO system significantly reduces the active antennas/RF chains compared to other benchmark schemes.

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.

Reconfigurable intelligent surfaces (RISs) have attracted extensive attention in millimeter wave (mmWave) systems because of the capability of configuring the wireless propagation environment. However, due to the existence of a RIS between the transmitter and receiver, a large number of channel coefficients need to be estimated, resulting in more pilot overhead. In this paper, we propose a joint sparse and low-rank based two-stage channel estimation scheme for RIS-assisted mmWave systems. Specifically, we first establish a low-rank approximation model against the noisy channel, fitting in with the precondition of the compressed sensing theory for perfect channel recovery. To overcome the difficulty of solving the low-rank problem, we propose a trace operator to replace the traditional nuclear norm operator, which can better approximate the rank of a matrix. Furthermore, by utilizing the sparse characteristics of the mmWave channel, sparse recovery is carried out to estimate RIS-assisted channels in the second stage. Simulation results show that the proposed scheme achieves significant performance gain in terms of estimation accuracy compared to the benchmark schemes.

Integrated dual functional radar and communication (DFRC) has been regarded as one of the most promising technologies. However, beamforming design in DFRC system is a challenge due to its nonconvexity. Inspired by the studies of active reconfigurable intelligent surface (RIS), we propose a joint transmitter and receiver design with adaptive beamforming for an active RIS aided DFRC system, where the active RIS is utilized to assist the DFRC to simultaneously detect targets and serve multiple users. Specifically, the WMMSE design criterion is exploited to design the transmit waveform of the DFRC by maximizing the sum-rate (SR) of users. Then, the Minorize-Maximization (MM) framework is derived to jointly optimize the amplification and phase shift coefficients of the active RIS. Simulation results show that when the active RIS is introduced, the SR of the system will improve greatly as compared to the scheme with passive RIS.

The Industrial Internet of Things (IIoT) has been viewed as a typical application for the fifth generation (5G) mobile networks. This article investigates the energy efficiency (EE) optimization problem for the Device-to-Device (D2D) communications underlaying unmanned aerial vehicles (UAVs)-assisted IIoT networks with simultaneous wireless information and power transfer (SWIPT). We aim to maximize the EE of the system while satisfying the constraints of transmission rate and transmission power budget. However, the designed EE optimization problem is nonconvex involving joint optimization of the UAV’s location, beam pattern, power control, and time scheduling, which is difficult to tackle directly. To solve this problem, we present a joint UAV location and resource allocation algorithm to decouple the original problem into several subproblems and solve them sequentially. Specifically, we first apply the Dinkelbach method to transform the fraction problem to a subtractive-form one and propose a mulitiobjective evolutionary algorithm based on decomposition (MOEA/D)-based algorithm to optimize the beam pattern. We then optimize UAV’s location and power control using the successive convex optimization techniques. Finally, after solving the above variables, the original problem can be transformed into a single-variable problem with respect to the charging time, which is linear and can be tackled directly. Numerical results verify that significant EE gain can be obtained by our proposed algorithm as compared to the benchmark schemes.

Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables resource-limited mobile terminals to offload part or all of the computation-intensive applications to nearby edge servers. On the other hand, by introducing reconfigurable intelligent surface (RIS), it can enhance the offloading capability of MEC, such that enabling low latency and high throughput. To enhance the task offloading, we investigate the MEC non-orthogonal multiple access (MEC-NOMA) network framework for mobile edge computation offloading with the assistance of a RIS. Different from conventional communication systems, we aim at allowing multiple IoT devices to share the same channel in tasks offloading process. Specifically, the joint consideration of channel assignments, beamwidth allocation, offloading rate and power control is formulated as a multi-objective optimization problem (MOP), which includes minimizing the offloading delay of computing-oriented IoT devices (CP-IDs) and maximizing the transmission rate of communication-oriented IoT devices (CM-IDs). Since the resulting problem is non-convex, we employ$\epsilon $-constraint approach to transform the MOP into the single-objective optimization problems (SOP), and then the RIS-assisted channel assignment algorithm is developed to tackle the fractional objective function. Simulation results corroborate the benefits of our strategy, which can outperforms the other benchmark schemes.

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) has been developed as a promising approach to enhance the performance of fifth-generation (5G) systems through intelligently reconfiguring the reflection elements. However, RIS-assisted beamforming design highly depends on the channel state information (CSI) and RIS’s location, which could have a significant impact on system performance. In this paper, the robust beamforming design is investigated for a RIS-assisted multiuser millimeter wave system with imperfect CSI, where the weighted sum-rate maximization problem (WSM) is formulated to jointly optimize transmit beamforming of the BS, RIS placement and reflect beamforming of the RIS. The considered WSM maximization problem includes CSI error, phase shifts matrices, transmit beamforming as well as RIS placement variables, which results in a complicated nonconvex problem. To handle this problem, the original problem is divided into a series of subproblems, where the location of RIS, transmit/reflect beamforming and CSI error are optimized iteratively. Then, a multiobjective evolutionary algorithm is introduced to gradient projection-based alternating optimization, which can alleviate the performance loss caused by the effect of imperfect CSI. Simulation results reveal that the proposed scheme can potentially enhance the performance of existing wireless communication, especially considering a desirable trade-off among beamforming gain, user priority and error factor.

Most recognizing occluded faces methods focus on synthetic-occluded faces for training due to the lack of real-occluded data. However, the performance may suffer from degradation since the synthetic-occluded and real-occluded face images are under different distributions. Hence, it draws our eyes to transfer the model from the synthetic to the real-world domain. In this paper, we propose a source data-free domain adaptive occluded face recognition framework to optimize the network in the target domain via redefining it as a pseudo labels denoising problem. To obtain reliable pseudo labels, we train synthetic-occluded and non-occluded images via distribution alignment to extract occlusion-robust features. Nonetheless, completely correct labels are still unattainable. Then, a denoising strategy is proposed to optimize pseudo labels by centroid-based feature clustering. Experiments show that the proposed approach can effectively recognize the real-occluded face; it also reminds the occluded faces recognition community about the feasibility of domain adaptation in existing tasks.

Plane spiral orbital angular momentum (PSOAM) mode-groups (MGs) and multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) serve as two emerging techniques for achieving high spectral efficiency (SE) in the next-generation networks. In this paper, a PSOAM MGs based multi-user MIMO-NOMA system is studied, where the base station transmits data to users by utilizing the generated PSOAM beams. For such scenario, the interference between users in different PSOAM mode groups can be avoided, which leads to a significant performance enhancement. We aim to maximize the energy efficiency (EE) of the system subject to the constraints of the total transmission power and the minimum data rate. This designed optimization problem is non-convex owing to the interference among users, and hence is quite difficult to tackle directly. To solve this issue, we develop a dual layer resource allocation algorithm where the bisection method is exploited in the outer layer to obtain the optimal EE and a resource distributed iterative algorithm is exploited in the inner layer to optimize the transmit power. Besides, an alternative resource allocation algorithm with Deep Belief Networks (DBN) is proposed to cope with the requirement for low computational complexity. Simulation results verify the theoretical findings and demonstrate the proposed algorithms on the PSOAM MGs based MIMO-NOMA system can obtain a better performance comparing to the conventional MIMO-NOMA system in terms of EE.

The intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication system has emerged as a promising technology for coverage extension and capacity enhancement. Prior works on IRS have mostly assumed perfect channel state information (CSI), which facilitates in deriving the upper-bound performance but is difficult to realize in practice due to passive elements of IRS without signal processing capabilities. In this paper, we propose a compressive channel estimation techniques for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity of mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel is converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multiobjective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed method achieves competitive error performance compared to existing channel estimation methods.

The system capacity can be remarkably enhanced with the help of intelligent reflecting surface (IRS) which has been recognized as a advanced breaking point for the beyond fifth-generation (B5G) communications. However, the accuracy of IRS channel estimation restricts the potential of IRS-assisted multiple input multiple output (MIMO) systems. Especially, for the resource-limited indoor applications which typically contains lots of parameters estimation calculation and is limited by the rare pilots, the practical applications encountered severe obstacles. Previous works takes the advantages of mathematical-based statistical approaches to associate the optimization issue, but the increasing of scatterers number reduces the practicality of statistical approaches in more complex situations. To obtain the accurate estimation of indoor channels with appropriate piloting overhead, an offset learning (OL)-based neural network method is proposed. The proposed estimation method can trace the channel state information (CSI) dynamically with non-prior information, which get rid of the IRS-assisted channel structure as well as indoor statistics. Moreover, a convolution neural network (CNN)-based inversion is investigated. The CNN, which owns powerful information extraction capability, is deployed to estimate the offset, it works as an offset estimation operator. Numerical results show that the proposed OL-based estimator can achieve more accurate indoor CSI with a lower complexity as compared to the benchmark schemes.

This paper investigates the energy efficiency (EE) optimization problem for device-to-device (D2D) communications underlaying non-orthogonal multiple access (NOMA) unmanned aerial vehicles (UAVs)-assisted networks with simultaneous wireless information and power transfer (SWIPT). Our aim is to maximize the energy efficiency of the system while satisfying the constraints of transmission rate and transmission power budget. However, the considered EE optimization problem is non-convex involving joint optimization of the UAV's location, beam pattern, power control and time scheduling, which is difficult to solve directly. To tackle this problem, we develop an efficient resource allocation algorithm to decompose the original problem into several sub-problems and solve them sequentially. Specifically, we first apply the Dinkelbach method to transform the fraction problem to a subtractive-form one, and propose a mulitiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm to optimize the beam pattern. We then optimize UAV's location and power control by applying the successive convex optimization techniques. Finally, after solving the above variables, the original problem is transformed into a single-variable problem with respect to the charging time, which is a linear problem and can be solved directly. Numerical results verify that the significant EE gain can be obtained by our proposed method as compared to the benchmark schemes.

In this paper, we propose a compressive channel estimation technique for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel estimation are converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and a sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multi-objective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed algorithm achieves competitive error performance compared to existing channel estimation 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.

As a prior knowledge, non‐local self‐similarity (NSS) has been widely utilised in ill‐posed problems. Actually, similar textures appear not only in a single scale, but also in different scales. Unlike most existing patch‐based methods that only explore NSS in the same scale, a multi‐scale patches based image denoising algorithm is proposed in this study. The authors have designed a multi‐scale strategy to expand the search space of block‐matching, which will increase the probability of finding more similar patches. After that, the weighted nuclear norm minimisation (WNNM) algorithm is employed to reveal latent clean patches. With the join of the multi‐scale framework, the performance of WNNM can be improved. The proposed algorithm can be used to solve NSS‐based image restoration tasks. In this study, mainly image denoising is studied, and its effectiveness is derived through experiments on widely used test images.

The low rank approximation for MR image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In spite of the great success of existing low rank approximation methods, these tend to lose the subtle edge texture when removing noise. It could degrade the image visual quality and affect the final clinical diagnosis. In this paper, a novel MR image denoising approach is proposed based on low rank approximation model and the Laplacian‐of‐Gaussian edge detector. In the proposed approach, a similarity evaluation scheme for noisy patch is employed to avoid the effect of the noise in the patch matching, and the details of the edge texture are preserved by the Laplacian‐of‐Gaussian edge detector. Experimental results show that the proposed approach is efficient and superior to some of the existing approaches in both objective criterion and visual fidelity. The proposed method can retrieve a clear MR image from the noisy one, with the detail of the edge texture, which could be very important in the clinical diagnosis.

Quadrature spatial modulation (QSM) is one of the most prevalent transmission techniques for future wireless mobile network due to its high spectral efficiency (SE) and low complexity. However, it is restrictive to downlink of the cellular network deployments. Therefore, this paper proposes QSM for the multiuser uplink data transmission that increases the SE of the network by deploying multiple antennas at the mobile user with only two radio frequency (RF) chains. Maximum likelihood (ML) decoder is used to detect the received signal and attains the optimal detection performance but it is impractical because of its high computational complexity. Consequently, this paper proposes compressed sensing (CS) based orthogonal matching pursuit (OMP) detection as it has a suboptimal performance with a low computational complexity which can be a practical solution in the high‐density uplink multiuser network. However, conventional OMP algorithm has a low estimation performance due to multiuser interference which corrupt channel matrix. Thus, this work design an equalizer that mitigates the multiuser interference and improve the detection performance by orthonormalizing the columns of channel matrix. Simulation analysis confirm that the proposed QSM technique based on CS detector outperforms the conventional schemes in terms of SE and bit error rate (BER).

A new algorithm is proposed for compressed sensingmagnetic resonance imaging (CS-MRI). The lp-norm (0 <; p ≤ 1) based adaptive regularization model is used for MRI. The algorithm is established by using a novel iterative shrinkage scheme. In the iteration, the quasi-Newton method is employed. In the shrinkage, the threshold is defined varyingly. Also, the parameter p is selected dynamically in the algorithm. Comparing with some certain state-of-the-art methods for the noisy case, the proposed algorithm provides a higher accuracy of the MR image reconstruction. The performance of the proposed algorithm is validated by the theoretical analysis as well as some experimental results.

In this letter, robust sparse signal recovery is considered in the presence of the symmetric α-stable distributed noise. An M-estimate type model is constructed by approximating the location score function of the noise. A reweighed iterative hard thresholding algorithm is proposed to recover the sparse signal. The basis functions for the approximation and the recovery performance of the proposed algorithm are discussed. Simulations are given to demonstrate the validity of our results.