Metasurface-aided near-field radio imaging is emerging as an essential part of the future mmWave communication systems. Adjusting the metasurface phase shift to generate multiple measurements can significantly increase the system imaging aperture and enhance the resolution. However, this imaging technique relies heavily on precise measurements, and high-precision sampling leads to expensive hardware costs and memory burdens. To address this issue, this paper draws inspiration from binary compressive sensing and proposes a method for imaging under one-bit quantization. First, we propose a dynamic metasurface-aided mmWave imaging system with one-bit sampling, which simplifies the received signal acquisition process and significantly reduces the hardware and storage requirements. Then, a fused binary compressive sensing model is developed with an additional total-variation norm penalty to promote target continuity and suppress artifacts in mmWave images. Subsequently, we employ the proximal binary iterative hard thresholding algorithm to optimize the joint sparsity and the total variation (TV) constraints. In addition, the hybrid ℓ1-TV constraint is introduced to solve the problem of unknown a priori sparsity of image, and the alternating direction multiplication method is designed for effective reconstruction. Finally, the simulation results show that the proposed algorithms can utilize the target features under binary measurements and achieve better imaging accuracy and focusing performance than the sparsity constraint-only methods.

Magnetic antennas, due to their unique operating mechanism, have demonstrated excellent anti-interference capability and signal penetration in complex environments, leading to their rapid development in recent years. However, its inherent narrow-band characteristics limit the data transmission rate and affect the practical application range. Based on the frequency splitting phenomenon, this paper proposes a method for expanding the bandwidth of magnetic antennas by using parasitic structures and presents the equivalent circuit at the transmitting end. By introducing parasitic coil at the transmitter (TX) side and optimizing the system characteristics, the transmission bandwidth is increased by about 3 times, while the peak power is decreased by only 2.1 dB. In this paper, the influence of parasitic coil layout on the bandwidth of magnetic antenna is systematically discussed by combining the theoretical analysis, simulation calculations and experimental verification. The results show that the reasonable adjustment of the parasitic separation can effectively control the degree of frequency splitting and realize the broadband characteristics of the magnetic antenna, which provides a new idea for its application in high-efficiency wireless communication.

Much exploration has been conducted to ensure the successful implementation of underground applications based on wireless underground sensor networks (WUSNs). However, most of them only focus on the communication aspect while neglecting location information. As a matter of fact, for scenarios such as earthquake and mine emergency rescue, it is necessary to achieve communication and positioning simultaneously. Nevertheless, accomplishing this goal is challenging, as the underground environment not only causes tremendous attenuation but also has limited hardware resources. Long range (LoRa) technology has high reception sensitivity and a simple structure, making it well suited for underground emergency rescue. Therefore, we propose a LoRa-based joint communication and localization system called LoRaAid. When a disaster occurs, the wearable LoRa device transmits sensing information to surrounding buried nodes. Through collaboration between nodes, diversity reception of signals and location acquisition of the target can be achieved simultaneously. Two different schemes are proposed to achieve correct demodulation, and the closed-form expression for bit error rate (BER) is also derived. While implementing communication, LoRaAid uses the mapping relationship between noise-reduced received signal strength indicator (RSSI) and distance to achieve trilateral localization. Numerous experimental results demonstrate that LoRaAid can enable long-range communication while achieving decimeter-level positioning accuracy.

Critical node detection is an important task to analyze network vulnerability and measure network security. Most existing methods either focus on non-cascading scenario from the perspective of network static properties or focus on cascading scenario from the perspective of network dynamic behavior. The problem of critical node detection is unexplored in both scenarios simultaneously. To this end, we formalize the critical node detection problem into a multi-objective problem in the two scenarios at the same time. Then, a multi-objective critical node detection algorithm named MOBSCND is proposed to effectively solve the transformed problem, which can provide multiple critical node solutions for decision-makers in both cascading and non-cascading scenarios. In MOBSCND, we design an effective initialization strategy, which can produce better initial individuals while ensuring population diversity. In addition, a local search strategy is proposed to accelerate the convergence of the population and improve the quality of population individuals. The experimental results on 12 datasets show that the convergence and diversity of the solution set obtained by the proposed method are all superior to the baseline algorithms.