This paper addresses the impact of asymmetric jitter, which arises from air turbulence or mechanical vibrations, on the three dimensional (3D) attitude angles of aerial intelligent reflecting surface (AIRS) mounted on unmanned aerial vehicle (UAV). To characterize these effects, a physics-based channel model based on the spherical wavefront assumption (SWA) is established. To mitigate the resulting performance degradation, we propose a novel continuous reflection phase based on the planar wavefront assumption (PWA), leveraging the concept of the Zadoff-Chu sequence. This design integrates a conventional reflection phase component with a spatial-frequency-bandwidth-dependent term, effectively broadening the bandwidth of the passive beam. Using the proposed continuous reflection phase, we analyze the normalized array gain function and average received signal power under UAV jitter, deriving approximate expressions for these metrics using Fresnel functions. The analysis demonstrates that the proposed reflection phase can expand the beam bandwidth to cover the potential range of jitter angles. Furthermore, a discrete reflection phase is designed based on the continuous version. Numerical results confirm that the beam bandwidth remains stable as the severity of asymmetric jitter increases, illustrating the effectiveness of the designed phases in mitigating UAV platform instability. Additionally, the results indicate that the proposed reflection phase can effectively compensate for performance degradation caused by UAV jitter.

Millimeter wave (mmWave) communication and intelligent reflecting surface (IRS) are both promising solutions for the next generation of wireless communication technology. In this article, the near-field channel models based on the spherical wave assumption and parabolic wave assumption are proposed for double-IRS assisted mmWave communication systems, where the parabolic wave model serves as an approximation of the spherical wave model. Under the parabolic wave assumption, the directional-dependant rayleigh distances and near-field reflection phases are investigated, and explicit expressions for the normalized array gains and path power gains are obtained using the sine integral. Based on the obtained path power gains, the suboptimal IRS rotation angles are also explored. We first consider the range limits of the activation conditions for the rotation angles, and then take the derivative of the explicit expression for the amplitude of IRS-assisted link to obtain the suboptimal rotation angles for different links. Using the designed near-field reflection phases, the approximate achievable rate is obtained and verified by numerical results. From these results, an interesting finding emerges: under reasonable IRS dimensions, the performance gains of two IRSs working noncooperatively are significantly greater than those of two IRSs working together. In conclusion, this work highlights the importance of IRS rotation angles and the intrinsic nature of double-IRS assisted communications.

Reconfigurable intelligent surfaces (RIS) have attracted significant attention due to their capability of establishing virtual line-of-sight (VLoS) links. This paper proposes a channel model for RIS-assisted millimeter wave (mmWave) communication systems that incorporate the effective aperture (EA) of RIS elements, the horizontal and vertical rotation angles of the RIS, the servomechanism limitations associated with these rotation angles and the activation criteria to constrain the feasible range of these rotation angles. To enhance the system performance, we jointly optimize the horizontal and vertical rotation angles of the RIS with the objective of maximizing the signal-to-noise ratio (SNR) based on the proposed model. An alternating optimization (AO) algorithm is developed to solve this problem efficiently. Specifically, the original optimization problem is decomposed into two subproblems corresponding to independent optimization of the horizontal and vertical angles, and closed-form optimal solutions are derived for each subproblem. Updating iteratively these closed-form solutions yields suboptimal horizontal and vertical rotation angles for the RIS. Moreover, a global optimal solution of closed-form to the original optimization problem is derived for the special case where the base station (BS) is positioned directly in front of the RIS. Numerical results demonstrate that the suboptimal rotation angles obtained by the AO algorithm closely approximate the optimal solutions. Furthermore, the proposed AO algorithm, which jointly optimizes both rotation angles, significantly outperforms the methods that individually optimize either the horizontal or vertical angle.

Intelligent reflecting surface technology (IRS) is emerging as a major innovation in wireless communications due to its unique advantages. It takes advantage of a large number of low-cost passive elements with adjustable phase-shift capabilities, which can reflect incident signals independently. When these elements work together, IRSs can achieve three-dimensional passive beamforming without the use of any transmit RF link. This mechanism not only enhances spectrum efficiency but also reduces the energy consumption of communication systems. Based on this advantage of IRSs, the paper explores IRS-assisted multiuser wireless systems, where IRSs are cleverly deployed between a multi-antenna access point (AP) and multiple single-antenna users. By jointly optimizing the transmission beamforming of active antenna array of the AP and the passive phase-shift beamforming of the IRSs, the objective is to minimize the total transmit power of APs, while ensuring that each user’s signal-to-interference-plus-noise ratio (SINR) requirement is met. The optimization problem is challenging to solve, as it is a nonconvex quadratically constrained quadratic programming problem, and the optimization variables are highly coupled with each other. To address this challenge, a low-complexity and efficient optimization algorithm, known as the linearized alternating direction multiplier method (LADMM) algorithm is proposed to address the transmit power minimization problem. The simulation results indicate that the LADMM algorithm provides superior system performance and significantly lower complexity compared to other existing methods.

Reconfigurable intelligent surface (RIS) has the potential to shape the wireless channel into an intelligent programmable wireless propagation environment. RIS-assisted millimeter wave (mmWave) technology is considered as a potential technology for sixth generation (6G) wireless communications. In this article, an RIS-assisted mmWave system is considered, and the corresponding physics-based channel model under the parabolic wavefront assumption, which is a second-order approximation to the spherical wavefront assumption, is established. Based on the parabolic wavefront assumption, the approximate closed-form expression of the direction-dependent Rayleigh distance is derived, which is a supplement to the classical Rayleigh distance. Also, the RIS reflection phase, consisting of a conventional far-field reflection phase and an addition near-field reflection phase, is obtained. The far-field phase compensates the phase variations from the mismatch in the azimuth and elevation angles, and the near-field phase compensates the phase variations caused by the distance differences from the transmitter/receiver to different RIS unit cells. Based on the conventional far-field reflection phase and the designed reflection phase, the received signal power is explored, and the approximate expressions are also obtained by using the Fresnel functions, which are validated by using numerical results. In addition, the numerical results show that the mmWave channel model under parabolic wavefront assumption and the corresponding near-field reflection phases are necessary to explore the RIS-assisted mmWave communication systems.

In this paper, we propose an efficient joint precoding design method to maximize the weighted sum-rate in wideband intelligent reflecting surface (IRS)-assisted cell-free networks by jointly optimizing the active beamforming of base stations and the passive beamforming of IRS. Due to employing wideband transmissions, the frequency selectivity of IRSs has to been taken into account, whose response usually follows a Lorentzian-like profile. To address the high-dimensional non-convex optimization problem, we employ a fractional programming approach to decouple the non-convex problem into subproblems for alternating optimization between active and passive beamforming. The active beamforming subproblem is addressed using the consensus alternating direction method of multipliers (CADMM) algorithm, while the passive beamforming subproblem is tackled using the accelerated projection gradient (APG) method and Flecher-Reeves conjugate gradient method (FRCG). Simulation results demonstrate that our proposed approach achieves significant improvements in weighted sum-rate under various performance metrics compared to primal-dual subgradient (PDS) with ideal reflection matrix. This study provides valuable insights for computational complexity reduction and network capacity enhancement.

Millimeter-wave radar holds significant potential for dynamic gesture recognition in contactless human-computer interaction, particularly in the Internet of Things and consumer electronics applications. However, a considerable challenge persists in filtering vast amounts of extraneous data from millimeter-wave radar echoes to isolate meaningful gesture features. We present a novel approach based on sparse representation principles to address this. We first generate a range-Doppler map of gestures using a two-dimensional (2-D) fast Fourier transform, then construct a Doppler-Time trajectory from aggregated data across multiple frames. Capitalizing on the intrinsic sparsity in the Doppler-time domain, we employ the orthogonal matching pursuit algorithm to refine a multidimensional feature sequence across time, Doppler, and range dimensions. Central to our approach is a dual-stream Transformer network that explores complex 2-D correlations in feature sequences via multihead self-attention mechanisms. This technique significantly improves gesture feature extraction efficiency and reduces data redundancy. The experimental results show that our model has an average recognition accuracy of 99.17% and a size of 0.17M, which is very suitable for application in embedded devices.

Dynamic gesture recognition using millimeter-wave radar has a broad application prospect in the industrial Internet of Things (IoT) field. However, the existing methods in the random dynamic interference environment, such as throwing objects and waving and easily cause wrong recognition. This article proposes a dynamic gesture recognition method based on a convolutional neural network (CNN)-Transformer network to solve this problem. First, we reshape the original echoes acquired by the frequency-modulated continuous-wave (FMCW) millimeter-wave radar into 3-D data blocks in terms of Chirps$\times $Samples$\times $Frames. And we employ the mean elimination method to eliminate the static interference. Second, we extract dynamic gestures’ distance and Doppler information with the 2-D fast Fourier transform and obtain the range-time map and Doppler-time maps. And we employ the coherent accumulation method to improve the signal-to-noise ratio (SNR). Third, we construct the CNN-Transformer network model for dynamic gesture recognition. The CNN is used to extract the local features of gestures, and multiple Transformer modules are stacked to extract deeper effective features. Finally, we build a data set for gesture recognition, including six kinds of dynamic gestures and two kinds of random interference. The experimental results show that the proposed method has a gesture recognition accuracy of more than 98% and 96% in the noninterference scene and the random dynamic interference scene, respectively, which are superior to the conventional recognition methods.

Due to the existence of wind and pressure variation, the unmanned aerial vehicle (UAV) will undergo pitch and roll wobbles in 3-D space, which will further induce the position offset of the aerial intelligent reflector surface (AIRS) deployed on the UAV platform. In this article, we consider the impact of the UAV pitch and roll wobbles and the effective AIRS structure, which is the projected structure of AIRS reflection unit in the signal propagation direction, on the channel statistical characteristics, and propose a novel AIRS-assisted beam channel model for mobile-to-mobile (M-to-M) multiple-input–multiple-output (MIMO) communication systems. In the proposed model, the relationship between the scattering gain of the AIRS unit and its size is also considered. Based the proposed model, the power scaling law of the AIRS-assisted link is derived, and it is demonstrated that the average received signal power is proportional to the square of the total geometric area of the AIRS in the far-field region. The simulation results show that the UAV wobbles with small angles significantly affects the spatial correlation function and the average received signal power, and the Doppler spread can be significantly reduced by increasing the number of AIRS reflection units. The simulation results also show that the effective AIRS structure in the signal propagation direction is essential in AIRS-assisted channel modeling and the proposed AIRS-assisted M-to-M MIMO communication system can obtain higher performance gain than traditional M-to-M MIMO communication system.

Intelligent reflecting surface (IRS) via using massive low-cost passive elements that can reflect the signals by adjusting phase shifts provides a cost-effective and energy-efficient solution to enhance the wireless communication system’s performance. In the article, we consider an IRS-aided multiuser multi-input–single-output (MISO) downlink system. We tackle the weighted sum-rate (WSR) maximization by jointly optimizing the active beamforming at the base station (BS) and the passive beamforming at the IRS. We first decouple the nonconvex optimization problem by the Lagrangian dual transform, then resort to fractional programming to address the active and passive beamforming optimizations. We develop the mirror descent (MD) method and the accelerated projected gradient (APG) method to solve subproblems. The simulation results show that the MD and APG algorithm get the comparable WSR gain and convergence speeds as existing methods, but with a significantly lower computational complexity.

Due to the large path loss in millimeter wave (mmWave) band, the transmission path between transmitter (Tx) and intelligent reflecting surface (IRS) is considered as a Rayleigh fading channel, and a physics-based channel model is proposed for IRS-assisted mmWave communication system in urban scenario. Also, the horizontal and vertical rotation angles of IRS and the relationship between the scattering gain of IRS reflecting unit and its effective aperture in the incident direction and the desired reflection direction are considered in the proposed model. For the considered communication scenario, the existing reflection phases, which are designed to align the virtual line-of-sight (VLoS) components among Tx, IRS, and receiver (Rx) with the LoS components between Tx and Rx, are not the appropriate reflection phases. Based on the proposed model, we first obtain the statistical phases of the virtual scattering components within a cluster by minimizing phase differences between different IRS reflection units, and then obtain the reflection phases by minimizing the phase differences of the derived statistical phases for all clusters. By comparing with the existing reflection phases, the designed reflection phases can significantly enhance the system performance gains of mmWave communications. Using the designed reflection phases, the expressions of received signal power and upper bound of ergodic sum capacity are derived in this paper, which are validated by using Monte-Carlo simulation results. Numerical results show that the proposed mmWave channel model could accurately simulate the propagation characteristics of IRS. Also, numerical results show that the performance gains of IRS-assisted systems are equivalent to that of large-scale communication systems without using IRS.

Dynamic gesture recognition using millimeter-wave radar is a promising contactless mode of human–computer interaction with wide-ranging applications in various fields, such as intelligent homes, automatic driving, and sign language translation. However, the existing models have too many parameters and are unsuitable for embedded devices. To address this issue, we propose a dynamic gesture recognition method (named “Gesture-mmWAVE”) using millimeter-wave radar based on the multilevel feature fusion (MLFF) and transformer model. We first arrange each frame of the original echo collected by the frequency-modulated continuously modulated millimeter-wave radar in the Chirps × Samples format. Then, we use a 2-D fast Fourier transform to obtain the range-time map and Doppler-time map of gestures while improving the echo signal-to-noise ratio by coherent accumulation. Furthermore, we build an MLFF-transformer network for dynamic gesture recognition. The MLFF-transformer network comprises an MLFF module and a transformer module. The MLFF module employs the residual strategies to fuse the shallow, middle, and deep features and reduce the parameter size of the model using depthwise-separable convolution. The transformer module captures the global features of dynamic gestures and focuses on essential features using the multihead attention mechanism. The experimental results demonstrate that our proposed model achieves an average recognition accuracy of 99.11% on a dataset with 10% random interference. The scale of the proposed model is only 0.42M, which is 25% of that of the MobileNet V3-samll model. Thus, this method has excellent potential for application in embedded devices due to its small parameter size and high recognition accuracy.

In this paper, we use directional antenna arrays instead of large-scale antenna arrays to combat the severe propagation path loss, and propose a novel beam channel model for millimeter-wave (mmWave) unmanned aerial vehicle (UAV) multiple-input multiple-output (MIMO) communication systems. The sparsity of mmWave channel and the directivity gain of mmWave antenna arrays are considered in the proposed model, and the antenna directivity gain is affected by the beamwidth, i.e., the narrower the beam, the higher the antenna directivity gain. Also, the beamwidth affects the number of active clusters, i.e., the wider the beam, the more the active clusters. Based on the proposed beam channel model, the influence of antenna directivity gain on the channel statistical characteristics including space-time correlation, frequency correlation function, and achievable rate is investigated. It is found that the directional antenna arrays can reduce Doppler spread caused by the movement of UAV and receiver and the mmWave communication systems using directional antenna arrays with high directivity gains can achieve an equivalent performance gain to that of the mmWave communication systems using omni-directional large-scale antenna arrays. Furthermore, considering the correlation between transmit and receive antenna elements, we obtain a closed-form expression of the tight upper bound of achievable rate, which is validated by using previously reported analytical upper bound and the simulation results.

Abstract Frequency diversity array (FDA) radar can provide full spatial coverage with stable gains within a pulse duration. Based on the FDA, the integrated radar‐communication system can perform multi‐directional communication and whole‐space detection. However, the embedded communication bits disrupt the correlation of the transmitting waveform of each element. Correspondingly, the range sidelobe level (SLL) of the multi‐dimensional ambiguity function increases significantly. To address this issue, a low‐sidelobe waveform for integrated radar‐communication systems based on the FDA was designed. Two techniques based on the subarray time delay are employed to reduce the SLL in range dimension. Both methods, however, lower the angular resolution. Thus, a tangent FM signal as the baseband waveform to improve the angular resolution was selected. Simultaneously, the received signal processing methods of radar and communication was designed. The performances of the designed waveform are verified by analysing the multi‐dimensional ambiguity function and the bit error rate. The simulation results reveal that the proposed method can maintain a good radar target detection capability and satisfy the communication function.

Intelligent reflecting surface (IRS) composed of a large number of low-cost passive reflecting elements has attracted significant attention from communication communities because of its ability to substantially improve the communication performance. In this paper, the aperture area and radiation pattern of the IRS reflecting elements are considered, and a novel geometry-based three-dimensional (3-D) wideband channel model is proposed for IRS-assisted unmanned aerial vehicle (UAV) communication systems. In the proposed model, the reflection phase is designed by jointly considering the aperture area of the IRS reflecting element and the propagation phases among UAV, IRS, and receiver (Rx), each IRS reflecting element is modeled as an anomalous reflector instead of a specular reflector, and large-scale IRS reflecting elements can jointly beamform the signal in a desired direction. Based on the proposed model, the effects of arbitrary trajectory of UAV and the number and the size of passive reflecting elements on channel statistical characteristics are considered, and the average received signal power and the ergodic sum capacity, which consider the impacts of the number and the size of passive reflecting elements, are also investigated. Furthermore, the path loss of the IRS-assisted link, which is in inverse proportion to the square of aperture area of IRS reflecting elements, is derived, and it coincides with the measured results in real outdoor scenarios. Analysis shows that the communication performance can be enhanced by increasing the number and the size of IRS reflecting elements, and it is validated by numerical results and Monte-Carlo simulation results.

In this article, a nonstationary 3-D wideband geometry-based stochastic model (GBSM) is proposed for low-altitude unmanned aerial vehicle (UAV) multiple-input–multiple-output (MIMO) communication systems. The proposed GBSM is a combination of Line-of-Sight (LoS) components, local multipath components (MPCs) scattering from the scatterers around the receiver (Rx), named as local scatterers, and far MPCs scattering from far scatterers, defined as not local scatterers, and uses 2-D one-ring and 3-D cylinder to mimic local scatterers as well as 3-D multiple confocal elliptic cylinders to mimic far scatterers. In this article, two-state continuous-time Markov chains (CTMCs) are introduced to model appearances or disappearances of the LoS components, local MPCs at the transmitter (Tx) installed on UAV, and far MPCs at the Tx and Rx, and the evolution process of the far MPCs is also investigated. The concept of the visibility region (VR) is introduced to model the birth and death processes of the local MPCs at the Rx, and the effect of the size of the VR on channel statistics is also considered. In the proposed GBSM, the inherited nature of the LoS components, the local MPCs and far MPCs, is considered, and the corresponding statistical properties are derived. The proposed nonstationary 3-D GBSM is validated by the measured results in terms of temporal correlation, and the numerical results show that the proposed 3-D GBSM is suitable for describing nonstationarity of the 3-D UAV-MIMO channel.

A User Equipment (UE) beamforming method is proposed for massive MIMO based stratospheric communications with multiple High Altitude Platforms (HAPs). The method leverages multiple-antenna UEs to mitigate interference from neighboring HAPs. No additional channel information exchange between UEs and HAPs is needed. The beamforming vector is obtained at the UE side by using the semi-definite relaxation based method. Both the Rician channel model and the HAP- MIMO channel model based on the dynamic evolution of scatters are adopted. Simulations under various environmental configurations demonstrate the performance improvement of the proposed method.

This paper proposes a three-dimensional (3-D) multiuser channel model for stratospheric multipath-fading channel. The birth-death process is used to model the non-stationary properties that scatterers “appear” and “disappear” on the array axes. The spatial correlation of multiuser High Altitude Platform (HAP) multiple-input multiple-output (MIMO) channel is investigated in this paper. This model can be used to estimate the acquired antenna separation at Stratospheric Base Station (SBS) and user to achieve uncorrelated channel matrix between two adjacent users. Numerical results show that the proposed model is able to capture characteristics as observed in measurements.