Yang Wang

With the advent of 6G networks, integrated sensing and communication (ISAC) is a key enabling technology for native perception in complex industrial Internet of Things (IoT) environments, which demands accurate ISAC channel models. However, most existing models mainly capture correlation at the cluster level, making it difficult to characterize finer-grained parameter relationships and intra-cluster variations, and lack an effective mechanism for bidirectional parameter coupling between communication and sensing channels in industrial IoT scenarios. This paper proposes a shared-cluster geometry-based stochastic ISAC (SC-GBSM-ISAC) channel model for industrial IoT. The model is parameterized using channel measurements at 38 GHz and 132 GHz in a high-clutter indoor factory (InF (A)) scenario. Compared with existing ISAC channel models, the proposed model has the following features. First, a closed-loop “sensing–communication–sensing” channel modeling framework is constructed based on 3GPP TR 38.901, which integrates bidirectional parameter interaction between communication and sensing channels. Second, by selectively reusing sensing-target clusters to construct shared clusters for communication-channel modeling, the model introduces a physically consistent shared-scattering structure while preserving the statistical characteristics specified in 3GPP TR 38.901. Third, by combining scatterer information inferred from the communication channel with a scenario-dependent sensing probability model, the sensing background component is reconstructed. Finally, measurement-simulation comparisons in an independent low-clutter InF (B) scenario demonstrate that the proposed model effectively reproduces bidirectional parameter coupling and primary spatio-temporal statistical metrics at 38/132 GHz, validating its accuracy and cross-scenario consistency in the measured scenarios.

Aircraft cabin communication takes on a key role in communication networks. Due to the increasing demand for high data rates among passengers, millimetre-wave (mmWave) and sub-Terahertz (sub-THz) bands, with abundant spectrum resources, are envisioned as promising options. Therefore, detailed channel measurements are required to understand significant channel characterization in aircraft cabins. This paper presents a comprehensive comparison and analysis of channel characterization at 28 GHz, 38 GHz, and 130 GHz based on extensive measurements conducted in an aircraft cabin. A total of 84 transmitter-receiver (Tx-Rx) positions are measured, covering both line-of-sight (LoS) and non-LoS conditions, with Tx-Rx distance ranging from 1 m to 10 m. Based on the measured data, both large-scale and small-scale channel characterization parameters are extracted and analyzed. Statistical models of path loss, shadow fading, Rician K-factor, root-mean-square (RMS) delay spread, and RMS angular spread are proposed. The characterization is analyzed using the power-delay-angular profile and power-angular spectrum. The multipath components are clustered using the density-based spatial clustering of applications with noise algorithm to analyze their intra-cluster delay spread and intra-cluster angle spread. In addition, this paper analyses the system capacity and outage probability, providing some basis for communication system design and planning. To the best of our knowledge, this paper is the first to have both mmWave and sub-THz measurements and analysis performed on an aircraft cabin.

The morphological similarity of trees and the unstructured, complex spatial distribution in forest environments result in a scarcity of distinct features, posing significant challenges for point cloud registration. In this work, we introduce a novel, straightforward, and robust algorithm for forest point cloud registration. Initially, we extract instances of tree stems from the scene as key points, utilizing the spatial distribution of adjacent key points to impose robust constraints. Subsequently, we introduce a new feature descriptor named the Circular Neighborhood(CN) Descriptor. This descriptor models vectors and matrices for all key points, encoding the distance information of neighboring key points within various fan-shaped areas around each key point to establish a comprehensive set of scene descriptors. Building on this foundation, we developed an innovative feature descriptor matching algorithm that incorporates descriptor distribution consistency and frequency match scores along with a triangle bitwise comparison method. This approach evaluates the geometric consistency and reliability probabilities between descriptors. Ultimately, we generate a maximal matching set to gauge the visual similarity between two point clouds and accurately estimate their relative poses and transformation matrix. We benchmarked our method against state-of-the-art methods across four diverse datasets, and our findings demonstrate that the Circular Neighborhood Descriptor offers superior adaptability in dense forest environments and significantly enhances registration efficiency. To disseminate our discoveries and contribute to the community, we make our code available on GitHub.

Adverse weather conditions such as snow, fog, and rain pose significant challenges to LiDAR-based perception models by introducing noise and corrupting point cloud measurements. To address this issue, we propose TripleMixer, a robust and efficient point cloud denoising network that integrates spatial, frequency, and channel-wise processing through three specialized mixer modules. TripleMixer effectively suppresses high-frequency noise while preserving essential geometric structures and can be seamlessly deployed as a plug-and-play module within existing LiDAR perception pipelines. To support the development and evaluation of denoising methods, we construct two large-scale simulated datasets, Weather-KITTI and Weather-NuScenes, covering diverse weather scenarios with dense point-wise semantic and noise annotations. Based on these datasets, we establish four benchmarks: Denoising, Semantic Segmentation (SS), Place Recognition (PR), and Object Detection (OD). These benchmarks enable systematic evaluation of denoising generalization, transferability, and downstream impact under both simulated and real-world adverse weather conditions. Extensive experiments demonstrate that TripleMixer achieves state-of-the-art denoising performance and yields substantial improvements across all downstream tasks without requiring retraining. Our results highlight the potential of denoising as a task-agnostic preprocessing strategy to enhance LiDAR robustness in real-world autonomous driving applications.

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.

Cooperative vehicle coordination at unsignalized intersections has garnered significant interest from both academia and industry in recent years, highlighting its notable advantages in improving traffic throughput and fuel efficiency. However, most existing studies oversimplify the coordination system, assuming accurate vehicle state information and ideal state update process. The oversights pose driving risks in the presence of state uncertainty and communication constraint. To address this gap, we propose a robust and comprehensive intersection coordination framework consisting of a robust cooperative trajectory planner and a context-aware status update scheduler. The trajectory planner directly controls the evolution of the trajectory distributions during frequent vehicle interactions, thereby offering probabilistic safety guarantees. To further align with coordination safety in practical bandwidth-limited conditions, we propose a context-aware status update scheduler that dynamically prioritizes the state updating order of vehicles based on their driving urgency. Simulation results validate the robustness and effectiveness of the proposed coordination framework, showing that the collision probability can be significantly reduced while maintaining comparable coordination efficiency to state-of-the-art strategies. Moreover, our proposed framework demonstrates superior effectiveness in utilizing wireless resources in practical uncertain and bandwidth-limited conditions.