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.

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.

Underwater acoustic covert communication (UACC) is a promising technology for improving the security of sensitive data transmission in the Ocean Internet of Things (OIoT). Compared with traditional UACC, autonomous underwater vehicles (AUV) are used to improve the performance of UACC by transmitting interference signals and relaying information. However, the UACC process of the covert transmitter is vulnerable to the detector’s detection, and the AUV-assisted UACC countermeasure process lacks theoretical analysis. Hence, we propose an AUV-assisted underwater acoustic covert communication game (AUACCG) model to study the countermeasure process between the AUV-assisted covert communicator and the detector in OIoT. Specifically, we utilize AUV transmitting the interference signals to reduce the detector’s capability, analyze the utility functions and strategy set of both sides and derive the equilibrium strategies. In addition, we analyze the concealment probability and the outage probability in UACC. Then, we derive the specific Nash equilibrium strategy in AUACCG. Simulation results show that our proposal can improve the underwater acoustic covert communication performance compared with the state-of-art works.

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.

With the wide applications of the Internet of Things (IoT) technologies in intelligent manufacturing, production safety, smart citie, and other fields, target location awareness has been the primary issue as it can be used to personnel and material positioning, electronic fence settings, daily attendance statistics, and so on. Wireless sensor networks (WSNs) positioning can become an indispensable part of these location-based IoT applications that benefit from the ubiquitous sensing and communication ability. For addressing the large positioning errors caused by uncertain WSNs, this article proposes an improved particle filter-based hybrid optimization (IPFHO) algorithm. After mapping the noisy wireless signal to uncertain target location, the preliminary positioning of mobile target is realized by improved particle filter, whose coarse accuracy over time can be further optimized with use of iterative search method. We evaluate the proposed algorithm under different noise levels, process noise variances and computation times in extensive simulations. The results indicate that the positioning accuracy of proposed algorithm can be effectively improved in the presence of wireless ranging errors and anchor node calibration errors. Compared with the positioning error 0.27 m estimated by pure filtering, the positioning error of proposed algorithm can be reduced to 0.19 m by combining the filtering and search methods. The platform experimental results, which are consistent with the trend of the simulation results, validate the superior accuracy of proposed algorithm compared with relevant positioning algorithms.

Underwater acoustic target recognition (UATR) is an important supporting technology for underwater information acquisition and countermeasure. Usually, ship radiated noise is covered by the underwater acoustic background and previous deep learning methods for this task rely on clear and effective acoustic features. We propose a novel network called AMNet to alleviate the problem in this letter. It consists of a multibranch backbone network coupled with a convolutional attention network. The proposed network is able to obtain the internal features of radiated noise from the time-frequency map of the original data. The convolutional attention network adaptively selects the effective features by weighting them against the global information of the time-frequency map to assist the multibranch backbone network in classification recognition. Experimental results demonstrate that our model achieves an overall accuracy of 99.4% (2.4% improvement) on the ShipsEar database.

Underwater nodes are prone to drift under the water flow action, which makes the topological structure of underwater wireless sensor networks (UWSNs) have great uncertainty. It is bound to bring about the occurrence of node nonuniform distribution over time. Given the obstacles and boundaries constrained areas, the intention of this article is to redeploy the drifted underwater nodes for regaining higher coverage rate. Hence, we propose a 3-D virtual force coverage algorithm (3D-VFCA). Inspired by the physical water flow action, the position evolution model of drifted underwater nodes is derived under the continuous gravity, buoyancy, propulsion, and resistance forces, which can reveal the mechanism of UWSNs deformation and coverage holes caused by the underwater node drift. Then, the coverage problem of dense and sparse node distributions is transformed into the optimization problem of weighted distance between node positions and clustering centers in the precoverage process, which can reduce large moving distances caused by node blind movements and solve the problem of inaccurate clustering centers caused by outliers. Furthermore, the improved virtual force algorithm is designed in consideration of underwater nodes, obstacles, and boundaries, which can drive the precovered underwater nodes to more optimal positions based on the adaptive moving distance per step. Finally, coverage performance evaluations of drifted underwater nodes are performed under different coverage algorithms, node numbers, and obstacles. The experimental results indicate that the proposed 3D-VFCA can improve the coverage rates in UWSNs.