The efficient exchange of perception information between vehicles and infrastructure is crucial for implementing vehicle-infrastructure (VI) collaborative intelligent driving. To address the high real-time requirements of VI communication and lack of intelligence and flexibility in VI cooperation, this study proposes an efficient collaborative perception method based on vehicle re-identification for VI collaboration scenarios. The real-time requirements of such scenarios are addressed and a lightweight vehicle re-identification network called ShuffleBNLSH is designed. This network is combined with a hash algorithm to quickly generate ID information for collaborative sensing targets. Based on the state of the VI communication channel, the network can adaptively extract the bit features of the perceived vehicle target, adjust the feature length, and quickly perform feature matching for vehicle target re-identification. To rapidly fuse the VI collaborative perception information combined with the re-identification results and LiDAR 3D perception information from the vehicle and infrastructure, we designed a mini-ICP algorithm that can automatically select feature points and perform point-cloud registration. Experimental results show that the amount of data transmitted by a single target in cooperative sensing can be as small as hundreds of bits during the fusion of sensing targets on the vehicle and infrastructure sides. This reduces the bandwidth requirements for fusing perception targets, accelerates feature transmission and matching, and expands the perception range of VI collaborative autonomous vehicles without GPS information.

As large-scale low Earth orbit (LEO) constellations continue to expand, the potential of their signal strength for positioning applications should be fully leveraged. For high-precision angle of arrival (AOA) estimation, current spectrum search algorithms are computationally expensive. To address this, we propose a method that downscales the 2D joint spectrum search algorithm by incorporating satellite ephemeris a priori information. The proposed algorithm efficiently and accurately determines the azimuth and elevation angles of NLOS (non-line-of-sight) signals. Furthermore, an NLOS virtual satellite construction method is introduced for integrating NLOS satellite data into the positioning system using previously estimated azimuth and elevation angles. Simulation experiments, conducted with a uniform planar array antenna in environments containing both LOS (line-of-sight) and NLOS signals, demonstrate the effectiveness of the proposed solution. The results show that the azimuth determination algorithm reduces computational complexity without sacrificing accuracy, while the NLOS virtual satellite construction method significantly enhances positioning accuracy in NLOS environments. The geometric dilution of precision (GDOP) improved significantly, decreasing from values exceeding 10 to an average of less than 1.42.

With the continuous construction of large-scale Low Earth Orbit (LEO) constellations, their potential for Global Navigation Satellite System (GNSS) applications has been emphasized. This study aims to derive an optimal positioning configuration formula based on the ratio of high-elevation and low-elevation satellites, which can improve the positioning accuracy and overcome the accuracy loss due to signal occlusion. A genetic algorithm is used to solve the optimal positioning configuration problem for large-scale satellite selection. Through a simulation using Starlink satellites currently in orbit, it is verified that the traditional recursive algorithm cannot be applied to satellite selection for large-scale constellations. The proposed formula has a similar accuracy to the Quasi-Optimal algorithm when there is no signal occlusion and the satellites are uniformly selected. However, the accuracy of the latter deteriorates significantly under signal occlusion. Our algorithm can effectively overcome this problem. Moreover, we discuss the effect of different types of obstructions on the accuracy loss. We find that the Quasi-Optimal algorithm is more sensitive to a single large-angle obstruction than multiple small-angle obstructions. Our proposed formula can reduce the localization accuracy degradation caused by signal occlusions in both scenarios.

This paper proposes a method for enhancing vehicle monocular three-dimensional perception using vehicle re-identification (Re-ID) and collaborative vehicle infrastructure systems (CVIS), aimed at enhancing the perception range and safety of the majority of intelligent connected vehicles currently using cameras. The method initially employs a monocular three-dimensional perception approach to extract images and rough three-dimensional information of traffic targets from the vehicle side. Following that, an adaptive compression method called Adaptive-FALSH is introduced, which, combined with vehicle Re-ID technology, enables efficient compression and correlation of vehicle Re-ID features. Ultimately, a perception fusion method dubbed HRHF is proposed, merging monocular three-dimensional detection results from the vehicle side and high-precision perception results from the roadside. This method quickly extend roadside perception results in real-time to the vehicle side, thereby enhancing the vehicle’s perception range. Experimental results demonstrate that this method efficiently merges three-dimensional target perception information from both vehicle and road sides using just a few hundred bytes, without the need for high-precision maps and GPS assistance. While reducing communication data volume, this method also effectively widens the perception range of intelligent connected vehicles.

It is practically challenging to achieve perfect channel state information (CSI) and ideal reconfigurable intelligent surface (RIS) hardware for the transmission design of RIS-assisted multiple-input multiple-output (MIMO) systems. To address this issue, incorporating phase errors, spatial correlation, and imperfect CSI into a unified outage-constrained beamforming optimization framework can be extended from i.i.d. Rician model to correlated Rician model. Subsequently, the outage constrained power minimizing problem is first transformed into a convex problem through Bernstein-type inequality method. Then, precoder and reflection beamforming are iteratively optimized under the alternate optimization (AO) framework. Especially, the scheme adopts semidefinite relaxation (SDR) technology and element iteration method to optimize the passive beamforming and achieve better performance than Gaussian randomization scheme. The simulation results demonstrate the effectiveness of the proposed scheme in two models. Compared to i.i.d. model, which is more sensitive to CSI errors, correlated model can provide more robust QoS guarantees for large CSI uncertainty sets. Phase errors undermine transmission power under the two models, especially in the i.i.d. model. Further, deploying more RIS elements can effectively improve the transmission power. Ultimately, the proposed approach has the potential to improve transmission robustness in practical scenarios.

Edge caching in wireless networks struggles to reduce backhaul burden and enhance edge node QoS under deep fading and blocking of offloading links. Intelligent reflecting surfaces (IRS) address this challenge by reconfiguring propagation environments to improve transmission link quality. This study formulates a network cost minimization problem with imperfect CSI, in which content placement and hybrid beamforming are jointly optimized to balance backhaul capacity and transmission power.The problem decomposes into two subproblems: 1. The first subproblem employs probabilistic content placement strategy based on Zipf-distributed popularity, where backhaul capacity is modeled as a function of cached content probabilities, and it is solved by leveraging Karush-Kuhn-Tucker optimality conditions; 2. Hybrid beamforming is tackled using alternating optimization. Specifically, fixing IRS phase shifts transforms the transmission power subproblem into a convex form to optimize precoder. Conversely, fixing precoder enables solving the reflection beamforming subproblem using a penalty-based convex-concave procedure (CCP) to handle non-convex phase constraints. Numerical results show that the proposed scheme significantly reduces transmission power and backhaul capacity compared to baseline schemes, yielding substantial network cost savings. This work advances caching optimization and transmission robustness in cost-efficient wireless networks under channel uncertainty, demonstrating practical gains via IRS-aided joint design.