With the rapid development of advanced wireless networks and the growing reliance on location-based services, the demand for high-precision passive localization of unmanned aerial vehicles (UAVs) in fields such as emergency rescue and the industrial Internet of Things (IoT) has become increasingly urgent. Localization technologies based on received signal strength (RSS) have emerged as a preferred solution due to their low cost and ease of deployment. However, traditional on-grid compressive sensing-based localization in multi-target scenarios suffers from grid mismatch, resulting in limited localization accuracy and poor robustness. To address this issue, this paper proposes a high-precision off-grid multi-target localization method for UAVs based on physical-model-driven block-sparse compressive sensing. First, by employing a Taylor approximation of the RSS path loss model, an off-grid block sparse model with energy coefficients and geometric offset parameters is established, which jointly characterizes the target energy and position information while alleviating discretization errors at the model level. Second, a physical-model-driven fast iterative shrinkage-thresholding algorithm (FISTA) is proposed. The proposed physical-model-driven shrinkage operator replaces the proximal mapping operator, and incorporates physical boundary constraints and a differentiated penalty mechanism into the iterative process, thereby mitigating overfitting arising from higher-order model errors. Extensive Monte Carlo simulation experiments demonstrate that the proposed method achieves significantly superior average localization accuracy under various signal-to-noise ratio (SNR) and target number scenarios, with approximately 35% improvement over traditional on-grid methods and 25% over standard off-grid methods. Furthermore, it exhibits no accuracy saturation at high SNRs, accompanied by remarkably enhanced robustness.

We consider a downlink multi-user scenario and investigate the use of reconfigurable intelligent surfaces (RISs) to maximize the dirty-paper-coding (DPC) sum rate of the RIS-assisted broadcast channel. Different from prior works, which maximize the rate achievable by linear precoders, we assume a capacity-achieving DPC scheme is employed at the transmitter and optimize the transmit covariances and RIS reflection coefficients to directly maximize the sum capacity of the broadcast channel. We propose an optimization algorithm that iteratively alternates between optimizing the transmit covariances using convex optimization and the RIS reflection coefficients using Riemannian manifold optimization. Our results show that the proposed technique can be used to effectively improve the sum capacity in a variety of scenarios compared to benchmark schemes.

This paper presents a new Wireless Power Transfer (WPT) approach by aligning the phases of a group of spatially distributed Radio Frequency (RF) transmitters (TX) at the target receiver (RX) device. Our approach can transfer energy over tens of meters and even to targets blocked by obstacles. Compared to popular beamforming based WPTs, our approach leads to a drastically different energy density distribution: the energy density at the target receiver is much higher than the energy density at other locations. Due to this unique energy distribution pattern, our approach offers a safer WPT solution, which can be potentially scaled up to ship a higher level of energy over longer distances. Specifically, we model the energy density distribution and prove that our proposed system can create a high energy peak exactly at the target receiver. Then we conduct detailed simulation studies to investigate how the actual energy distribution is impacted by various important system parameters, including number/topology of transmitters, transmitter antenna directionality, the distance between receiver and transmitters, and environmental multipath. Finally, we build an actual prototype with 17 N210 and 4 B210 Universal Software Radio Peripheral (USRP) nodes, through which we validate the salient features and performance promises of the proposed system.