To address the challenge of high end-to-end delay in Flying Ad Hoc Network (FANET) under communication blackout scenarios, this paper proposes a Deep Reinforcement Learning (DRL)-assisted Double-Hop Information Enhanced Routing Protocol (DHRP). The proposed protocol models the routing process as a Markov Decision Process (MDP) to enable effective decision-making. In constructing the state space, it incorporates both node location information and link channel capacity, while considering network information within a two-hop neighborhood. Centered on a deep value network, the protocol employs a reward function that reflects realtime network dynamics to guide the agent in selecting the optimal next-hop node. Simulation results show that, compared to existing approaches, DHRP significantly reduces the average end-to-end delay in FANET under communication blackout conditions. Furthermore, DHRP demonstrates strong adaptability and robustness across various node densities and levels of network congestion by leveraging realtime environmental awareness and an intelligent decision-making mechanism to maintain overall network performance.

Based on the principle of Orthogonal Time Frequency and Space (OTFS) modulation, this paper designs an OTFS waveform scheme based on Zero Suffix (ZP) protection. Methods of synchronization, channel estimation, and the detection algorithm based on delay-time domain Maximum Ratio Combining (MRC) are presented, and the MRC detection algorithm is simplified. Hardware implementation schemes of channel interpolation and the MRC detector are given. And the Field Programmable Gate Array(FPGA)hardware implementation of the proposed OTFS system waveform is carried out to verify the feasibility of the key algorithms of the designed OTFS system. Test results show that the designed OTFS system has good performance in resisting doubly selective fading.

The rapid development of intelligent transportation systems has intensified the demand for real-time and highly reliable computing services, driving the evolution of vehicular edge computing toward more dynamic and flexible collaborative architectures. Multi-layer aerial networks overcome the inherent limitations of traditional ground infrastructure in terms of coverage and service continuity, emerging as a promising supplement and development trend for vehicular edge computing. To this end, a multi-layer aerial edge computing architecture integrating High Altitude Platform (HAP) and Unmanned Aerial Vehicle (UAV) is proposed, collaboratively providing efficient computing support for moving vehicles in the Internet of Vehicles(IoV). To address frequent aerial cell handovers caused by vehicle mobility, a novel handover-aware mechanism is introduced to predict the time window for cell switching under UAV coverage. Under the energy constraints of both vehicles and UAV, the bandwidth partitioning, computing resource allocation, and task offloading decisions are jointly optimized to minimize total task latency and mitigate handover-induced service interruptions. Moreover, to tackle the high computation complexity of the Mixed Integer Nonlinear Programming (MINLP) problem, a three-step iterative algorithm is designed. This algorithm decomposes the problem into subproblems of bandwidth allocation, computing resource allocation, and offloading decision optimization, which can be solved using the CVX tool, linear relaxation, and Alternating Direction Method of Multipliers (ADMM), respectively. Simulation results demonstrate that compared to baseline schemes, the proposed solution reduces total task latency by 11.9%, 23.3% and 25.5% for task sizes ranging from 5~9 Mb, respectively.

Skin cancer and melanocytic nevus share numerous similarities, which can result in a misdiagnosis by dermatologists. To improve the screening accuracy of early skin cancer patients, the Gamma Transform Block (GMTB) based on Gamma Transform (GT) and Wavelet Convolution Block (WTCB) based on Wavelet Transform (WT) are proposed. Furthermore, the Space-Frequency Transform Network (SFTNet) for capturing fine-grained features of skin cancer is innovatively proposed based on the Detection Transformer(DETR) architecture. SFTNet-based skin cancer screening system can effectively improve disease detection accuracy because it enhances the sample image at different channels and reduces over-fitting effect during the model training process. Simulation results on HAM10000 dataset show that the accuracy of this system can reach 85.5%, which underscores the significant clinical value of our approach in skin cancer assisted diagnosis.

Multiple Input Multiple Output (MIMO) technology significantly enhances signal transmission rates and system reliability through multi-antenna systems. To improve spectral efficiency and anti-interference capabilities, spatial modulation technology, as an extension of MIMO, has been proposed and widely applied. Generalized Spatial Modulation (GSM) further integrates multiple modulation schemes, enhancing the system's performance. Polar codes, as an efficient error correction code, leverage channel polarization to transform physical channels into virtual channels with varying levels of reliability, thus effectively improving the performance of MIMO and spatial modulation systems. This paper presents a decoding scheme for multi-user polar codes, aimed at optimizing the decoding process in the uplink Polar Coded-Generalized Spatial Modulation (PC-GSM) system. By combining the channel polarization characteristics of polar codes with the advantages of GSM, the scheme improves decoding algorithms, enhancing the reliability and data transmission rate of multi-user systems. Simulation results show that the proposed decoding scheme significantly boosts system performance, providing a novel solution for the integration of multi-user polar codes and spatial modulation technology.

Chaos-based communication technology has emerged as a research hotspot in recent years due to its superior resistance to multipath fading and robust security features. Differential Chaos Shift Keying (DCSK), as a non-coherent digital modulation scheme, has attracted widespread attention. However, in practical communication scenarios, the increasing demand for reliable data transmission has revealed the limitations of traditional DCSK systems, such as low transmission rates and high Bit Error Ratios (BER), highlighting the urgent need to enhance system reliability. Considering the significant advantages of polar codes, including low complexity and near-capacity performance, this paper delves into the integration of polar coding algorithms with chaos modulation techniques based on channel polarization principles, aiming to further improve the reliability of chaos-based communication systems. Experiment results show that the proposed solution significantly improves the reliability of chaos-based communication systems and keeps its feature of low complexity.

With the vigorous development of Internet of Things technology, a large number of terminal devices have been widely deployed. As a result, the challenging issues of energy replenishment for massive terminal devices and the congestion of the frequency spectrum have become increasingly prominent. These not only limit the further development of the Internet of Things but also pose a severe challenge to existing network infrastructure. Low-power Internet of Things, as a key technology to address these issues, has received extensive attention from researchers. As a result, a survey on low-power Internet of Things is studied in this paper. Firstly, an overview of low-power Internet of Things is provided, including its principles and various low-power communication technologies. Secondly, based on existing research achievements, main transmission architectures of low-power Internet of Things are analyzed. Subsequently, aiming at the complex communication environment in the Internet of Things, communication architectures of low-power Internet of Things under different propagation environments are presented. Then, typical application scenarios of existing low-power In ternet of Things are discussed, demonstrating its potential value in multiple fields. Finally, future research trends of low-power Internet of Things are prospected and outlined.

Existing key point detection algorithms tend to suffer from reduced detection precision, missed detections, or misaligned key points in scenarios with varying lighting conditions and dense crowds with overlapping figures. To address this issue, an improved LBW-YOLOv8n-Pose algorithm for multi-person pose estimation in complex environments is proposed based on YOLOv8n-Pose. By introducing the Large Separable Kernel Attention (LSKA) in the Spatial Pyramid Pooling-Fast (SPPF) layer of the feature extraction backbone network, the algorithm enhances the image feature representation and perception capabilities. A weighted Bidirectional Feature Pyramid Network (BiFPN) is incorporated in the neck network for reconstruction to improve the multi-scale feature fusion effect. Additionally, an improved Wise-IoU loss function is adopted to accelerate the model's convergence speed and enhance its robustness in complex scenarios. Experimental results show that the improved model achieves precision, recall, and average detection precision of 85.7%, 76.8%, and 81.7% respectively on the MS-COCO2017 human key point dataset, representing significant improvements over the original model. Moreover, it can more accurately and effectively detect key point information of multiple people in complex situations.