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.