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.