Gesture recognition is the central enabler of human-computer interaction (HCI). In addition to the semantic information contained in gestures, gesture-based user identification can effortlessly enhance HCI system security. Recently, the Wi-Fi-integrated sensing and communication (ISAC) technology has shown great potential in a field hitherto occupied by computer vision and radar sensing. In this work, leveraging Wi-Fi sensing, we propose a system called WiGesID that achieves joint gesture recognition and human identification (JGRHI). The basic idea behind WiGesID is to identify personalized spatiotemporal dynamic patterns from the gestures of different users. Moreover, we develop an effective approach to recognize new categories of gestures and users by computing relation scores between the features of the new category samples and the support samples. To evaluate the performance, we implemented WiGesID and conducted extensive experiments. The results demonstrate that our system outperforms the state-of-the-art method for cross-domain sensing and accurately recognizes new categories, which promotes the use of this application of Wi-Fi sensing in HCI.

Deep learning (DL) has been widely applied in automatic modulation classification (AMC), while the superb performance highly depends on high-quality datasets. Motivated by this, the AMC under few-shot conditions is considered in this letter, where a novel network architecture is proposed, namely automatic modulation classification relation network (AMCRN), and verified with the baseline methods. Experimental results state that the accuracy of proposed AMCRN exceeds 90% and 10% to 50% improvements are obtained compared with classical schemes when the signal-to-noise ratio (SNR) is greater than -2 dB.

By leveraging massive available data and hidden communication patterns, deep learning (DL) has enabled diverse applications in wireless network operations. In this paper, we consider radar-aided beam prediction in multi-input multi-output (MIMO) communication systems with federated transfer learning (FTL) to preserve users' location privacy. Specifically, we propose a novel structure, i.e., radar-aided federated transfer beam prediction (RaFT-BP), to achieve few samples-enabled distributed beam selection in internet of vehicles (IoV) scenarios. Simulation results show that the proposed RaFT-BP can achieve the 93.78% top-5 accuracy with 600 samples in the distributed node, enabling 11.9% to 33.2% beam selection accuracy improvement compared with baseline schemes.

Efficient and green transmission of communication and sensing (C&S) signals is a vital problem in Internet of Things (IoT) networks. In this article, we propose a variational autoencoder (VAE)-empowered deep learning (DL) network to fuse and reconstruct the integrated C&S signals. Specifically, we present a convolutional neural network to fuse the input communication data and SAR images into a combined representation, which can then be transmitted to other nodes in space–air–ground–ocean-integrated IoT networks. Instead of directly transmitting C&S data, the transmission of a fused feature vector can greatly save network resources and reduce network burden. Then, a mirrored deconvolutional network is constructed to recover C&S data from the transmitted feature representation. An end-to-end unsupervised training strategy is considered to train the proposed DL network without any label information and human labor. Qualitative and quantitative experiments demonstrate the feasibility of our proposed approach for transmitting and reconstructing integrated C&S signals. Further analysis on hyperparameter sensitivity and loss functions verifies the necessity and efficiency of the components in the proposed DL model. Note that the proposed efficient fusion and reconstruction schemes for C&S signals may provide the convenience to information sharing under the distributed scenarios.

The increasing number of smart vehicles is leading to an increasing scarcity of spectrum resources for the internet of vehicles (IoV), which has given rise to an urgent requirement for automatic modulation classification (AMC) in cognitive radio (CR) systems. Meanwhile, for the flexibility of unmanned aerial vehicles (UAVs), the AMC implemented based on UAVs is considered an effective method to achieve reliable communication between intelligent vehicles. However, for distributed UAV task implementation, real-time radio data needs to be transmitted between UAVs and a cloud server. This process requires maintaining a high-capacity, secure channel environment, which is difficult to accomplish. In this paper, we propose a federated transfer learning framework to implement AMC in a distributed scenario, which avoids radio data transmission in each UAV. To reduce data dependence, the pre-trained deep learning (DL)-based model is sent to each UAV node and performs transfer learning, which brings more focused learning of the channel environment in which various UAVs are located. The simulation results show that federated transfer learning-based AMC offers better recognition accuracy than centralized approach. Compared to the centralized training methods, the federated transfer learning algorithm achieves an improvement of 1.04% to 12.05% in classification accuracy for each node with less training data. Besides, the effect of different fine-tuning layers on the accuracy is investigated, showing that fine-tuning three layers could achieve optimal accuracy. Additionally, different numbers of UAVs are employed to verify the impact on the results. The experimental results show that the number of UAVs can improve the results but to a limited extent. Furthermore, we evaluate the proposed method by various measurements, such as accuracy, precision, and F1-score. Accordingly, compared with the baseline methods, the proposed scheme achieves an improvement of 1% to 14% over them.

Movable antennas (MA) and reconfigurable intelligent surfaces (RIS) are recognized as the pivotal technologies for integrated sensing and communication (ISAC) system. Although MAs overcome the gain limitations of fixed-position antennas via exploiting spatial degrees of freedom, RIS enhances signal propagation through programmable phase manipulation. However, existing ISAC research predominantly relies on far-field channel models and conventional optimization methods, which suffer from suboptimal performance in high-frequency near-field scenarios and exhibit high computational complexity when handling coupled continuous-discrete variables. To address these limitations, this paper proposes a novel MA-enabled RIS (MA-RIS) architecture under near-field spherical wave propagation conditions. The proposed system jointly optimizes the weighted sum rate of communication users while ensuring sensing performance through Cramér-Rao bound constraints. Unlike traditional alternating optimization frameworks, we develop a deep learning-based solution including four specialized neural modules: 1) a feature extractor for channel state information, 2) an antenna position vector optimizer, 3) a beamforming matrix optimizer, and 4) a RIS phase matrix optimizer. This integrated architecture enables joint optimization of spatial antenna configurations, beamforming parameters, and RIS phase shifts while accounting for near-field distance-dependent channel characteristics. The simulation results reveal that the proposed scheme achieves significant performance enhancements of 5%∼10% compared to conventional fixed-position RIS configurations, while maintaining comparable effectiveness with the benchmark alternating optimization (AO) approach, which demonstrates our method’s superior capability in balancing system adaptability with optimization efficiency.