Radio interference sources mounted on high-speed mobile platforms present a considerable challenge to accurate localization and tracking due to their wide-ranging and dynamic characteristics. This article tackles the intricate problem of tracking erratically maneuvering interference sources and introduces a dynamic tracking scheme for the spatial decomposition of motion states. Firstly, the proposed scheme decomposes the complex three-dimensional motion into three independent one-dimensional linear movements. This facilitates the concurrent application of interacting multiple model matching filters across all three dimensions, enabling precise characterization of the complex motion states. Secondly, an enhancement is made to the update process of the noise covariance matrix within the real-time noise estimator. Specifically, a time-varying exponential forgetting factor is incorporated to ensure the robustness of the adaptive cubature Kalman filter algorithm when combined with the noise estimator. Simulation results show that the proposed tracking scheme outperforms traditional methods in terms of both the speed of tracking error convergence and the fluctuations reduction. The improved accuracy and reliability of the proposed scheme position it as a promising approach for addressing the challenges associated with tracking dynamic radio interference sources on high-speed mobile platforms.

With the emergence of the sixth generation (6G) era, ubiquitous sensing and machine intelligence have emerged as the primary motivation for the development of large-scale Internet of Things (IoT) systems. However, the limited communication capabilities of IoT devices are often constrained by costs and further inhibit their full potential. Nowadays sensor technologies are rapidly evolving, offering improved accuracy and update rates for environmental perception. Considering this, the challenge faced by 6G IoT lies in the increasing data payload from sensing and the constrained communication capacity. To this end, lite communication solutions for IoT are investigated in this paper. By integrating task-oriented communication concepts with sensing objectives, we extract and represent semantic information from sensor nodes. Subsequently, this information is transmitted to edge nodes via wireless transmission for analysis and processing of sensory data. This approach effectively reduces redundant data transmission, thus achieving lite communication in IoT. Based on these principles, a lite edge task-oriented communication system architecture tailored for IoT applications is proposed. Specifically, we implement and evaluate the IoT communication module and the edge task-oriented module, demonstrating that the proposed system reduces resource consumption by 99 % and significantly alleviates the transmission payload of IoT. Through leveraging low-speed IoT transmission protocols and high-precision sensors, our system effectively supports a wide range of sensing tasks.

Inspired by Dirty Paper Coding (DPC), in this paper we propose a design approach of transmit beamforming for the integrated sensing and communications (ISAC) system that offers simultaneous sensing services via radar and information delivery to users. Specifically, we shape the channel matrix into an equivalent lower triangular matrix. Under this strategy, the communicating users are able to eliminate the multi-user interference (MUI) by using successive interference cancellation (SIC). The corresponding transmit beamforming optimization problem subject to radar constraints is formulated and then solved by a manifold optimization approach. Compared to the conventional design, the idea behind our proposal is to reduce partial burden of beamforming towards interference suppression while taking advantage of low-complexity detection at the receiver to achieve better performances. The simulation results show that our proposed scheme has lower BER than the conventional scheme at low-SNR regime. As for sensing function, the beam pattern of proposed scheme has only a small performance loss compared with the desired beam pattern.

Reconfigurable intelligent surface (RIS) technologies can effectively extend the coverage of MIMO systems with improved signal-to-interference-and-noise ratio (SINR). However, the channel estimation of RIS-assisted MIMO systems is very challenging, though crucially important. First, the passive reflective nature of RIS makes it hard to obtain channel state information (CSI) of the transmitter-to-RIS and RIS-to-receiver links, respectively. Second, limited by the number of transmit antennas, reflective elements typically need to be divided into groups, and RIS needs to turn on/off element groups successively to obtain the full CSI, causing extra overhead and difficulty in control. To tackle these problems, we first propose a Two-fold-SVD based Separated Reflecting Channel Estimation (2-SVD-SRCE) scheme, which based on pilot blocks can effectively extract transmitter-to-RIS and RIS-to-receiver links’ CSI, respectively. Then, we propose a novel pilot reconstruction design at the transmitter and develop a Pilot Reconstruction based Separated Reflecting Channel Estimation (PR-SRCE) scheme, which can overcome the limitation that the number of reflective elements in each group cannot exceed transmit antennas. Analyses and simulations show that our proposed scheme can significantly simplify the RIS control procedures, while achieving good performances in both complexity and channel estimating accuracy.

As drones become increasingly prevalent in human life, they also raise security concerns such as unauthorized access and control, as well as collisions and interference with manned aircraft. Therefore, ensuring the ability to accurately detect and identify between different drones holds significant implications for coverage extension. Assisted by machine learning, radio frequency (RF) detection can recognize the type and flight mode of drones based on the sampled drone signals. In this paper, we first utilize Short-Time Fourier Transform (STFT) to extract two-dimensional features from the raw signals, which contain both time-domain and frequency-domain information. Then, we employ a Convolutional Neural Network (CNN) built with ResNet structure to achieve multi-class classifications. Our experimental results show that the proposed ResNet-STFT can achieve higher accuracy and faster convergence on the extended dataset. Additionally, it exhibits balanced performance compared to other baselines on the raw dataset.