This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.

In this paper, a light emitting diode grouping based spatial modulation (LGSM) scheme is investigated for the indoor visible light communication (VLC) system for a better symbol error rate (SER) performance with the aid of characteristics of the spatial modulation. In consideration of the high channel correlation property of the visible light communication, the multiple transmitting LEDs are separated into different groups with the purpose of alleviating the channel correlation among each group. Owing to the proposed grouping scheme, the spatial information and channel correlation property can be perfectly utilized to improve the system performance of SER, which implies a higher transmission capability by the proposed LGSM scheme. Meanwhile, the mathematical derivation of SER for the proposed LGSM scheme is also provided by considering the maximal likelihood detection. Both of the simulated and theoretical results have proved that a better SER performance can be obtained via the proposed LGSM scheme compared with the common spatial modulation VLC system. Furthermore, the influence of the elevation angle of the receive photo detector in the LGSM VLC system is also investigated.

The modern smart grid is a vital component of national development and is a complex coupled network composed of power and communication networks. The faults or attacks of either network may cause the performance of a power grid to decline or result in a large-scale power outage, leading to significant economic losses. To assess the impact of grid faults or attacks, hardware-in-the-loop (HIL) simulation tools that integrate real grid networks and software virtual networks (SVNs) are used. However, scheduling faults and modifying model parameters using most existing simulators can be challenging, and traditional HIL interfaces only support a single device. To address these limitations, we designed and implemented a grid co-simulation platform that could dynamically simulate grid faults and evaluate grid sub-nets. This platform used RTDS and EXata as power and communication simulators, respectively, integrated using a protocol conversion module to synchronize and convert protocol formats. Additionally, the platform had a programmable fault configuration interface (PFCI) to modify model parameters and a real sub-net access interface (RSAI) to access physical grid devices or sub-nets in the SVN, improving simulation accuracy. We also conducted several tests to demonstrate the effectiveness of the proposed platform.

Addressing the challenging problem of detecting small targets in space, an infrared small target detection algorithm that incorporates foreground moving targets and deep learning is introduced. By running the foreground motion target detection algorithm and the deep learning YOLOv5 target detection algorithm, the regions where the motion target is located are obtained respectively. Then, the suspicious region of motion small targets is focused on and framed through region comparison. To verify the effectiveness of the algorithm, an infrared image dataset was constructed, and the experimental results demonstrate that the designed algorithm can effectively achieve intelligent detection of infrared small targets from kilometers away. This offers substantial support for practical applications in the fields of space exploration, deep space exploration, and security monitoring. It holds substantial application value for the integrated air and space monitoring and detection of small targets in space.

In this paper, a power allocation problem at secondary users (SUs) to maximize the overall system throughput is addressed in a non-orthogonal amplify-and-forward (NAF) cooperation assisted non-orthogonal multiple access (NOMA) network. Due to the nonlinearity of optimization problem, a cooperative multi-agent Q-Learning based power allocation algorithm (CQPA) is proposed under both sum and individual power constraints of SUs with relay function. To further reduce the computational complexity, a neighboring strategy searching based power allocation algorithm (NSPA) is additionally proposed. Simulation results demonstrate the efficiency of proposed algorithms, where a valuable trade-off between throughput and computational complexity can be achieved by the NSPA.

Malicious certificate authorities (CAs) pose a significant threat to the security of the Internet of Things (IoT). Existing CA auditing schemes primarily rely on passive detection and public data collection, lacking real-time and comprehensive monitoring. In this paper, we propose a novel double-authentication preventing signature scheme based on an SM2 algorithm, referred to as Dap-SM2. We further enhance its functionality by introducing Self-C2AD, a CA auditing mechanism with self-enforcement. In our proposed mechanism, any malicious CA that generates two certificates with different descriptions (such as public key and basic information) for the same IoT device will immediately lose its private key. To ensure the security of our proposed scheme, we provide a detailed security analysis of Dap-SM2. The analysis demonstrates that our Self-C2AD mechanism meets the necessary security requirements, offering robust protection against malicious CAs. Additionally, we conduct an efficiency evaluation and present experimental data to illustrate the promising potential of our construction for future IoT applications. By introducing the Dap-SM2 scheme and the Self-C2AD mechanism, we address the critical issue of malicious CAs in the IoT domain. Our approach provides real-time and comprehensive auditing capabilities, surpassing the limitations of existing schemes. The security analysis confirms the effectiveness of Dap-SM2, while the efficiency evaluation and experimental data demonstrate its suitability for practical IoT applications. In summary, our work presents a novel solution to combat the threat of malicious CAs in the IoT context. The Dap-SM2 scheme, coupled with the Self-C2AD mechanism, offers enhanced security and real-time auditing capabilities. The security analysis validates the robustness of our approach, while the efficiency evaluation and experimental data showcase its potential for future IoT deployments.