Yifeng He

This paper tackles the problem of performance limitations on channel estimation accuracy in near-field massive MIMO systems resulting from the sparse reconstruction method with fixed spatial grid dictionaries. It presents a deep unfolded network with a learnable sparse dictionary (LSD-UnfoldNet) to address the grid mismatch, thereby obtaining high accuracy and low-complexity near-field channel estimation as the sparse dictionary and channel reconstruction parameters are jointly optimized using end-to-end learning. The iterative shrinking threshold algorithm is unfolded into an L-layer neural network with L learnable sparse dictionaries and linear transformation matrices in each layer of the network. Besides, to improve the correlation characteristics of the sensing matrix, an orthogonal regularization term is introduced. During training of the neural network, the Adam optimizer and cosine annealing learning rate scheduling are applied to jointly minimize the normalized mean square error and total loss of learned dictionary correlations. Experimental results demonstrate that the proposed method, at a representative signal-to-noise ratio of 20 dB, achieves normalized mean square errors of −24.5 dB. The root mean square errors for angle and distance estimation were 0.50° and 0.18 m, respectively, achieving superior performance relative to heuristic algorithms and deep learning-based algorithms. Beyond its strong robustness to different grid granularities and spatial distances, the proposed method also achieves competitive pilot overhead compared with other approaches.

Wireless self-organizing network is a network system that does not rely on ground infrastructure, and has a wide range of applications due to its dynamic topology, strong survivability, and other characteristics. In recent years, with the complexity and diversification of wireless ad hoc network application scenarios, the scale of nodes in wireless ad hoc networks has also increased. In large-scale wireless ad hoc networks, how to access channels, allocate and use channel resources reasonably for a large number of nodes has become an urgent problem to be solved. From publicly available information, there is currently relatively little research on channel access technology for large-scale node wireless ad hoc networks, and most of it still remains in theoretical research. This article mainly studies and designs a wireless ad hoc network channel access architecture that can support 128 nodes based on the above issues, and implements the wireless ad hoc network channel access architecture on a hardware platform. The test results show that the access architecture designed in this article can ensure a success rate of first time chain building delay application of less than 2 seconds in the case of full node access, meeting the design indicators.

An oscillator is a key component of a satellite communication measurement and control system, performing symmetry precisely as a time frequency reference. At the same time, the phase noise index has a close coupling relationship with the overall performance of the entire system, while persistently breaking the symmetry property of the oscillator during work. It is very important to study and reasonably allocate the phase noise index. According to the theoretical formula of phase noise, this paper analyzes the power law spectral model in the frequency domain and the noise jitter characteristics in the time domain. Using the carrier tracking loop in the measurement system, the frequency domain transfer model of phase noise is established, and typical analysis results are given. A discrete fractional integration algorithm is proposed, which can generate the phase noise time domain sequence under the given power law spectral model coefficients. The proposed algorithm is more realistic compared with the previous numerical calculation method, and has sufficient accuracy compared with the results of the instrument. After frequency domain conversion, the RMS deviation between the simulated noise sequence in the frequency domain and the measured single sideband power spectral density is less than 2.5 dB, indicating that the phase noise sequence can reflect the frequency domain characteristics more completely. A communication measurement simulation system is built, and a discrete sequence simulation analysis method combining frequency domain and time domain is provided, and the coupling relationship of key indicators such as phase noise, thermal noise, communication data rate, modulation method and bit error rate is synthesized. The results show that the BER of the QPSK/BPSK communication system will not be significantly reduced if the phase jitter RMS caused by the phase noise is less than 5 degrees, so 5 degrees can be used as a reference for the decomposition of the carrier SSB phase noise index. The simulation results have been successfully applied to a satellite inter-satellite link system, which has universal practical application value.

With the rapid development of network technology and computer technology, network applications can be seen everywhere. From life, finance to politics, the development of all walks of life has been inseparable from computer communication networks. Once there is a problem with the computer communication network, it will cause serious economic losses and may even cause national turmoil. Therefore, the reliability requirements of computer communication networks are getting higher and higher, and the reliability analysis and application of computer communication networks have become the focus of research. Based on genetic algorithm, this paper optimizes the reliability of computer communication networks. Firstly, the theory of reliability of computer communication network is briefly introduced, and the factors affecting reliability are simply analysed. Secondly, based on the influencing factors, the reliability optimization analysis model of computer communication network is established. Finally, the genetic algorithm is used to solve the established reliability optimization analysis model. The experimental results show that with the increase in the number of iterations of the genetic algorithm for data regression calculation, the algorithm reaches more than 0.99, and the optimal solution is obtained, which improves the reliability of the network, and the adaptability of the network link also increases. The genetic algorithm is applied to the reliability analysis of computer communication network, and the corresponding reliability optimization method is designed, which can improve the reliability of computer network while reducing the cost.

In today’s society, due to the development of science and technology, human demand for satellite communication is higher and higher. At present, most places and departments in China have established their own unique transmission mode. Wireless data transmission system is a new efficient, safe and reliable information transmission mode. However, there are a series of problems in traditional wired signal transmission, such as serious waste of resources and complex network, which can not meet the communication requirements under high-speed payload. Therefore, the research and design of new schemes to improve satellite transmission capacity is of great significance and value to the development trend of today’s society. Therefore, this paper designs a satellite ultra-high speed payload data transmission system based on convolutional coding technology. Firstly, this paper introduces the concept of satellite ultra-high effective data transmission, then expounds the development process of data transmission, and studies the application of convolutional coding technology. Then the system structure of satellite ultra-high speed payload data transmission is designed, and the performance of convolutional coding technology of the system is tested. Finally, the test results show that convolutional coding technology is very efficient in identifying wrong coding, and can effectively improve the efficiency of information reception.