Xin Xie

Extremely Large-Scale Multiple-Input Multiple-Output (XL-MIMO) is positioned as a transformative technology for sixth-generation (6G) networks, effectively turning base stations into high-resolution sensing and communication hubs. However, the practical deployment of XL-MIMO is hindered by the “curse of dimensionality,” specifically the prohibitive overhead associated with Channel State Information (CSI) sensing and feedback, alongside the computational latency of massive antenna arrays. To resolve the conflict between high-resolution sensing requirements and limited bandwidth resources, this paper proposes a novel two-stage beamforming architecture that synergizes physics-aware dimensionality reduction with deep learning. First, by exploiting the inherent sparsity of XL-MIMO channels in the angle-delay domain, we design a Spatial–Frequency Concentration Block (SFCB). This module functions as a hard-attention sensing mechanism, performing efficient source-end dimensionality reduction on raw CSI at the User Equipment (UE) via precise feature extraction and adaptive energy truncation. Second, we develop a highly adaptable Direct Integrated Precoding Network (DIP-I). Departing from the conventional “sense-reconstruct-then-precode” paradigm, DIP-I learns end-to-end mapping to directly regress the optimal precoding matrix at the Base Station (BS). Comprehensive simulations utilizing the COST 2100 and QuaDRiGa hybrid channel models demonstrate that, under a massive 512-antenna configuration, the proposed framework achieves exceptional beamforming gain. Furthermore, it significantly reduces sensing data overhead and inference latency, offering a superior trade-off between spectral efficiency and hardware resource consumption for future 6G sensing-communication integrated systems.

Network slicing has emerged as a pivotal technology in addressing the diverse customization requirements of the Industrial Internet of Things (IIoT) within 5G networks, enabling the deployment of multiple logical networks over shared infrastructure. Efficient resource management in this context is essential to ensure energy efficiency and meet the stringent real-time demands of IIoT applications. This study focuses on the scheduling problem of minimizing average transmission power while maintaining Age of Information (AoI) tolerance constraints within 5G wireless network slicing. To tackle this challenge, an improved Dueling Double Deep Q-Network (D3QN) is leveraged to devise intelligent slicing schemes that dynamically allocate resources, ensuring optimal performance in time-varying wireless environments. The proposed improved D3QN approach introduces a novel heuristic-based exploration strategy that restricts action choices to the most effective options, significantly; reducing ineffective learning steps. The simulation results show that the method not only speeds up convergence considerably but also achieves lower transmit power while preserving strict AoI reliability constraints and slice isolation.

Integration of sensing, communication, computing, and intelligence (ISCCI) represents a pivotal advancement in B5G and 6G technologies, offering transformative potential for the Internet of Vehicles (IoV). As IoV systems become increasingly integral to intelligent transportation and autonomous driving, these systems also face escalating security challenges across multiple layers, including physical, network, application, and system dimensions. (1) This paper comprehensively surveys these security issues, systematically analyzing the threats encountered at each layer and proposing targeted countermeasures to mitigate risks. (2) Furthermore, the paper explores future trends in IoV security, emphasizing the roles of 6G networks, blockchain technology, and digital twins in addressing emerging challenges. (3) Finally, based on a comprehensive review of current research and insights, this paper aims to serve as a foundational reference for advancing secure and sustainable IoV ecosystems.