Research on Efficient Channel Decoding Algorithm for Memory Channel and Short Packet Transmission in Smart Grid - Qin, Mingwei; Gao, Yongxiang; Hou, Baolin; Wang, Huan; Zhou, Wenmao; Yao, Yuancheng (Frontiers in Energy Research, 2022)
More and more smart factories use smart grid for power system automation, and its wireless control technology requires low time delay and high reliability communication. However, the traditional channel coding and decoding algorithm has shortcomings in decoding performance for the short packet transmission process of memory channel in factories. GRAND algorithm has outstanding short packet error correction performance. In the decoding process, the order of noise parameter combination affects the decoding delay. Aiming at the communication problem of smart grid in the process of factory power supply and distribution, this paper analyzes the characteristics of the original noise parameter ranking algorithm. When the steady-state flip probability is large, more search times are required to obtain the correct combination of noise parameters, which means that greater delay is required for decoding in time-varying channel. To solve the above problems, this paper optimizes the noise parameter ranking before the noise error mode arrangement, and proposed a noise parameter ranking algorithm for predicting the symbol string. Firstly, the channel perception is completed by edge calculation, and the channel parameters b and g are obtained , then based on the geometric distribution characteristics of the noise of the interference sequence, the mean value of the noise symbol string is predicted, and then the number of noise symbols is obtained. At the same time, in order to reduce the impact on the performance of noise parameter ranking algorithm when the steady-state flip probability is small, a reordering mechanism is designed, which searches left and right based on the predicted noise parameters. Based on this mechanism, the original noise parameter ranking matrix is reordered according to the predicted noise parameter combination. Simulation results show that the proposed algorithm has better search performance than the original sorting algorithm. Finally, by comparing the BM Decoding Algorithm with different noise parameter ranking algorithms of guess decoding, the results show that the noise parameter ranking algorithm proposed in this paper has better decoding performance in the environmental channel of smart factory, so as to improve the reliability of smart grid in the process of factory power supply and distribution.