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.

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.

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.

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.

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.

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.

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.

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.