Driven by the ever-increasing supply of computational resources, the parameter size of Large Language Models(LLMs) continues to expand and their task performance in natural language processing has become more superior. However, there are still limitations when faced with reasoning problems, especially in common-sense reasoning or mathematical problems. Chain of Thought(CoT) significantly improves its ability to solve problems in different domains by guiding the model to generate reasoning steps. In this paper, we not only sort out the theoretical foundation system and technical evolution of CoT from the perspective of training method, but also further discuss application scenarios such as government service and enterprise digitalisation. Finally, in the light of the development trend of Artificial Intelligence (AI), the paper discusses the essential role of CoT in the development of LLMs towards a higher cognitive level from the perspective of the degree of AI, and points out the challenges and technical bottlenecks that need to be solved at the present time.

In this paper, a Wireless Powered Communication Network (WPCN) based on Active Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (ASTAR-RIS) is proposed. The communication network is mainly composed of four parts: Power Station (PS), Sensor Node Groups (SNGs), ASTAR-RIS and Access Point (AP). The operation process of the communication system is mainly divided into two stages: Wireless Energy Transfer (WET) stage and Wireless Information Transfer (WIT) stage. Energy Splitting (ES) mode is adopted in the wireless energy transmission phase, and Time Switching (TS) mode is adopted in the wireless information transmission phase. This paper aims to optimize the phase shift parameters and communication resource allocation of ASTAR-RIS to maximize system throughput. Since the optimization problem is non-convex, this paper uses an alternate optimization algorithm to solve the problem. Firstly, the problem is divided into two parts according to the coupled variables. The optimal solutions of the variables in these two parts are solved by Semidefinite Relaxation (SDR) and Fractional Programming (FP) respectively. Experimental results show that the communication scheme proposed in this paper can provide higher performance gain for the system.

In future 6G Internet of Things (IoT) systems, extensive deployment of pivotal technologies such as high-frequency millimeter waves and terahertz spectrum makes it possible for wireless transmission among network devices situated in the near-field region. As a byproduct, Dynamic Metasurface Antenna (DMA) and other small-sized antenna arrays have been widely applied in this scenario due to their advantages in transmission efficiency, physical size, and power consumption. And related research has received increasing attention. Aiming to improve the energy performance of receivers in near-field wireless transmission, a downlink near-field wireless Simultaneous Wireless Information and Power Transfer (SWIPT) system based on DMA is proposed. Under the condition of satisfying the minimum transmission rate requirements of all information users, an efficient solution for jointly optimizing the tunable frequency response matrix of DMA and the digital precoding vector is proposed for this optimization problem. In addition, the influences of factors such as the distance between users and the minimum Signal to Interference plus Noise Ratio (SINR) on the system performance are also discussed on this basis. Simulation results show that the scheme proposed in this paper can effectively improve the joint performance of wireless information and power transmission compared with other existing technologies.

This paper investigates an Intelligent Reflecting Surface (IRS)-assisted Physical-layer Key Generation (PKG) system under residual Transceiver Hardware Impairments(THI). A closed-form expression for the Key Generation Rate(KGR) is derived, and a KGR maximization problem is formulated under the base station transmit power constraint and the unit-modulus constraint on the IRS phase shifts. To solve this problem, a robust optimization algorithm is proposed, which integrates Alternating Optimization(AO), Successive Convex Approximation (SCA), Semi-Definite Relaxation(SDR), and penalty methods to iteratively optimize the transmit beamforming and IRS phase shifts. Numerical simulation results demonstrate that the proposed robust algorithm can effectively mitigate the impact of hardware impairments and improve the KGR.

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.

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.

The Internet of Things (IoT), as one core area of 6G development, plays a crucial role in driving network architecture changes and supporting core application scenarios. However, IoT systems suffer from energy imbalances and short network lifecycles, which severely restrict the improvement of data collection efficiency. With the rise of Unmanned Aerial Vehicle (UAV) technology, its high maneuverability can effectively construct Line of Sight (LOS) communication links, thereby improving communication speed. This has great application value in data collection of IoT systems and can solve the problem of low data collection efficiency caused by the short lifecycle of IoT networks. To this end, UAVs are used to collect data from ground IoT devices and build a data collection and transmission link for air-to-ground collaboration. An intelligent data collection method based on Deep Reinforcement Learning (DRL) is proposed. In addition, a predictive neural network is designed to further improve data collection efficiency by predicting network data at the Base Station (BS) side, thereby achieving the goal of reducing IoT device energy consumption and extending network lifespan. Simulation results show that the proposed data collection algorithm has good performance advantages in terms of device energy consumption and energy balance, and is superior to traditional data collection algorithms. At the same time, the proposed data collection network architecture can extend the network lifespan by 1.2 times when the predicted data accounts for 12.5%. In addition, simulations have shown that the designed predictive neural network outperforms other compared networks in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE) metrics.

Reconfigurable Intelligent Surface (RIS) and Rate Splitting Multiple Access (RSMA) technologies are two emerging communication techniques with broad prospects in future wireless systems. Active RIS (ARIS) has the advantage of overcoming the effects of multiplicative fading compared to Passive RIS (PRIS). The paper addresses the outage performance of ARIS-assisted RSMA systems under random deployment. Under Nakagami-m small-scale fading model in downlink, multiple users are randomly distributed within a semicircular region around ARIS and sorted by the distances between the users and ARIS. Taking the optimal phase shift for the furthest user as the entry point, a moment matching approach is employed to derive the shape and scale parameters associated with the cascaded channel power characteristic for each RSMA user. We derive a closed-form expression for the outage performance of the users, analyze the effect of the power splitting coefficients on the Outage Probability (OP), and obtain the diversity order and an expression for the approximate OP. Simulation results show that the OP of a non-perfect phase-shifted user under ARIS-assistance is re duced by 58% compared to the passive RIS-assisted system under a power budget of 50 dBm and a moderate amount of RIS elements. In addition, the power consumption of the user with the optimal phase shift is reduced by 53 dBm.

In contemporary society, Global Navigation Satellite System (GNSS) has become an essential tool for daily travel, significantly improving travel efficiency. However, in environments with weak signals, such as indoors or tunnels, GNSS systems often experience signal loss due to insufficient signal strength, leading to positioning failure and inability to provide accurate navigation services. To address this challenge, this paper proposes a high-precision positioning solution based on an improved Extended Kalman Filter (EKF). This solution integrates Ultra-Wideband (UWB) least squares method, KF, and EKF technologies, and introduces a Multi-Innovation EKF (MIEKF) algorithm. By utilizing multi-time observation data and a forgetting factor mechanism, the solution effectively reduces positioning errors and enhances positioning accuracy. Experimental results show that the root mean square error of this solution can be reduced to 0.179 m, verifying its high-precision positioning capability in weak signal environments and providing reliable technical support for precise navigation in complex scenarios.

In smart grids, the presence of numerous high-power electrical devices and communication sensing equipment severely hinders signal transmission in positioning systems. To address the challenge of accurately locating weak signals in a Reconfigurable Intelligent Surface (RIS)-assisted Non-Orthogonal Multiple Access (NOMA) system under interference from multiple base stations and communication users, this paper considers the impact of multiple small base stations and multiple users in a smart grid environment. A horizontal positioning error of the target user is used as the evaluation metric. While ensuring the Quality of Service (QoS) for communication users, the proposed method jointly optimizes base station power, multi-user interference, and power allocation factors. The Lagrangian dual method and sub-gradient approach are employed to solve the non-convex optimization problem caused by multiple users and small base stations. Simulation results demonstrate that, under the same resource allocation, the proposed RISNOMA integrated sensing and communication system significantly reduces the average positioning error compared to traditional NOMA-based system.

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.

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.

Seepage monitoring is crucial for the safe operation and maintenance of dams. Traditional dam observation methods suffer from significant random errors and insufficient inspection frequency during flood seasons. To address these limitations, this study proposes an infrared thermography-based unmanned aerial vehicle inspection system for detecting surface seepage on dam bodies. First, an image dataset of seepage-affected areas on the dam surface was collected and established using an infrared camera. Then, an improved Mask Region-based Convolutional Neural Network(Mask R-CNN) framework was employed to extract seepage region data, enabling rapid detection of surface seepage. Subsequently, binary processing was applied to quantify the seepage area. Finally, the proposed method was validated on the downstream face of a hydropower station. Experimental results demonstrate that the proposed approach reduces the inspection cycle by 80% compared to traditional methods while maintaining sufficient accuracy for routine dam monitoring. This study provides a novel technique for seepage detection and quantitative analysis, offering a new solution for dam leakage inspection and seepage-related damage assessment.

In recent years, Transformer-based visual models (e. g. , Swin Transformer) show good prospects in visual tasks, however, these methods usually focus on reducing signal distortion between original and reconstructed data, while ignoring perceptual quality. Considering that the conventional Mean Square Error (MSE) loss fails to reflect perceptual and semantic quality effectively, we propose a weighted loss function combining MSE and Learned Perceptual Image Patch Similarity (LPIPS), and accordingly construct a Swin Transformer-based semantic communication framework, called Swin Transformer with LPIPS-based Joint Source-Channel Coding (STL-JSCC) method, which significantly enhances image reconstruction quality and semantic consistency. For performance evaluation, two semantic-aware metrics are introduced: the Images Semantic Deviation (ISD) value and Iamges Semantic Similarity(ISS). These indicators form a joint perceptual-semantic evaluation system, which breaks through the limitations of traditional evaluation methods. Experimental results show that the proposed STL-JSCC outperforms other models in all the indexes, verifying the significant potential and advantages of the proposed method in improving the image reconstruction quality and semantic extraction capability.

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.

Compared with a different-color backgrounds, recognizing and detecting cucumber fruits under uniform-color backgrounds remains a key challenge due to limited distinguishing features and increased susceptibility to occlusion and background interference. To address this, we propose YOLO-ACG, a detection network based on YOLOv11n. An Adaptive Dynamic Downsample (A-Down) module is introduced, combining deformable convolution and channel attention to achieve adaptive cross-scale feature sampling. A Ghost_HGNetV2 architecture is designed, where the High-resolution Group Stem (HGStem) reduces input channels to extract efficient intrinsic features, and the Ghost_HGBlock applies knowledge distillation to enhance feature representation. A Context and Spatial Feature Calibration Network (CSFCN) network structure is introduced, which includes Context Feature Calibration (CFC) and Spatial Feature Calibration (SFC). The CFC module aggregates context information relevant to each pixel, while the SFC module leverages calibrated spatial features to ensure accurate understanding of spatial layout the image. Together, they enable the network to more precisely distinguish cucumber fruits from backgrounds with similar colors. Experimental results show that the improved model achieves 4.64 percentage points increase in precision, recall by 5.07 percentage points, F1 by 4.89 percentage points, and mAP by 4.48 percentage points. Ablation and comparative experiments confirm that YOLO-ACG significantly reduces false positives and missed detections, offering effective technical support for cucumber fruits recognition in complex, uniform-color environments.

To address the challenges of data scarcity, stylistic diversity, and complex textures in art image classification, a novel self-supervised learning framework is proposed——Frequency-Masked Contrast (F-MaCo). Built upon a dual-branch contrastive learning paradigm, F-MaCo leverages a two-dimensional Discrete Wavelet Transform (DWT) to project images into the frequency domain, enabling dynamic frequency-domain masking augmentation. Additionally, a perceptual loss-driven weighting mechanism is introduced to effectively capture the multi-scale features and rich textures information of art images. Experimental results demonstrate that F-MaCo achieves state-of-the-art performance on four art image datasets—MAMe, Kaokore, Artbench10, and ArtDL—with Top1 accuracies of 73.72%, 77.38%, 58.38%, and 68.31%, respectively, validating its effectiveness and robustness in art image representation learning.

Focusing on the channel estimation accuracy degradation caused by the beam splitting effect in near-field wideband Extremely-Large Scale Multiple Input Multiple Output (XL-MIMO) systems, this paper proposes a Bi-Directional Integrated Multi-Subcarrier Augmented Bilinear Pattern Detection (BDI-MSABPD) algorithm. Built upon the polar-domain sparse representation framework, the proposed method addresses both the sparse support set misalignment and parameter estimation bias induced by beam splitting through a dual mechanism combining explicit polar-domain resolution enhancement and implicit multi-subcarrier joint optimization. Simulation results demonstrate that the BDI-MSABPD achieves an average reduction of 2 dB in Normalized Mean Squared Error (NMSE) compared with conventional Bilinear Pattern Detection (BPD) algorithm.

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.

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.

When a carrier (such as drones, ships, and vehicles) moves in extreme environments, the visibility of satellites may be lost, leading to a temporary or prolonged loss of lock on Global Navigation Satellite System (GNSS) signals. In such scenarios, an integrated navigation system is forced to switch to a pure Inertial Navigation System (INS). However, prolonged reliance on inertial navigation alone results in the accumulation of errors and a rapid decline in navigation accuracy. To address the rapid decline in INS accuracy after GNSS signal loss, a fusion navigation technology of GNSS and INS assisted by Transformer networks is proposed. When the GNSS signal is locked, the Transformer network utilizes current INS information and GNSS incremental data (the change in GNSS position information between two adjacent time periods) to train a mapping relationship between the two. When the GNSS signal is lost, the Transformer network leverages the previously established mapping relationship to predict GNSS incremental information based on the current INS data, and then integrates the INS information with the predicted GNSS data for navigation. Simulation results demonstrate that the Transformer network-assisted GNSS/INS fusion navigation technology can provide stable and reliable navigation signals even under conditions of temporary or prolonged GNSS signal loss. Furthermore, the Transformer network-assisted fusion navigation method offers a reference for other network-assisted fusion implementations.

To address the issue of current command and control network key node recognition methods relying on expert knowledge, a method based on convolutional neural networks from the perspective of communication reconnaissance is proposed. Powerful feature extraction capabilities of convolutional neural networks are leveraged to develop an intelligent paradigm for key node recognition. First, the communication relationship information between nodes is transformed into a multi-dimensional information matrix using feature engineering. Then, inspired by the Finite Impulse Response (FIR) filter structure, a Finite Impulse Response Squeeze and Excitation (FIRSE) neural network is proposed. Finally, a dynamic peak detection method is introduced to improve the training strategies and obtain optimal neural network parameters. Experimental results show that compared with typical machine learning and deep learning-based recognition methods, the proposed method offers higher identification accuracy.

In industrial networks, more and more intelligent applications put forward new requirements for deterministic guarantee ability of end-to-end transmission. Most of current research works on deterministic technologies focus on guarantee of their own network certainty, but ignore end-to-end determinism requiring cooperation of multiple deterministic technologies. Based on application scenario of nuclear power industry, a wide-area end-to-end deterministic network architecture with Flexible Ethernet (FlexE) and Time-Sensitive Networking (TSN) fusion is proposed, and an architecture is elaborated from two aspects of control plane function and data plane cooperative scheduling. In real wide area network environment, based on the dual-motor collaboration and industrial machine vision application scenario, the performance of the architecture is tested and verified. Results show that the architecture can meet the needs of applications for wide-area end-to-end deterministic networks, and offer good application value.

Predicting trajectories of key individuals plays an important role in preventing potential criminal activities, optimizing emergency response, and intelligence analysis. Application of this technology by public security departments helps maintain social stability, improve urban management efficiency, and improve economic development. However, existing techniques face challenges in adapting to dynamic environments, neglecting the scope of social influence, and influence quantification of neighborhood moving objects. A novel model for predicting long-term trajectory areas of key individuals based on destination-intention learning by integrating spatio-temporal queries, is proposed. Firstly, aiming to solve the problem of capturing the spatio-temporal features of moving object trajectories, a key individuals trajectory prediction model called Spatio-Temporal Multiple Attention (STMA) is introduced. It can enhance the model sensitivity to the change of behavioral features by capturing temporal dependencies and spatial interactions through temporal and spatial attention modules, respectively. Secondly, in order to cope with the problem of quantifying the social influence, a social force function is constructed to simulate the social influence of pedestrians. The virtual contour construction method and the social force function can accurately simulate dynamic behaviors and improve the efficiency of influence capture. Experiments based on real-world traffic datasets show that, compared to the state-of-the-art trajectory prediction algorithms, STMA demonstrates higher accuracy and reliability in long-term and short-term trajectory prediction. In terms of long-term forecasting, the STMA model achieves an average accuracy rate of 54.3%, outperforming Sophie by 29.3%, Social Spatio Temporal Graph Convolutional Neural Network (S-STGCNN) by 13.4%, Conditional Generative Neural System (CGNS) by 36.8%.

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.