| Citation: | ZOU W L，JIANG Y Z，HUANG Z，et al. Stack-bucket algorithm for convolutional codes based on dynamic optimization regulation[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（11）：3059-3065 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0772
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Long constraint length convolutional codes are used in the fields of satellite communication due to their strong anti-interference and difficulty in decipherment. However, there are shortcomings of low space utilization and high decoding complexity in the low signal-to-noise ratio environment. To overcome the above problems, this paper proposed a stack-bucket algorithm (DORSB) based on dynamic optimization regulation. The proposed algorithm uses a new parameter depth factor to assist path access, which can increase the path advantage near the end of the code tree and reduce the decoding complexity. When the stack overflows, the size of the bucket is regulated to reuse the bucket space and reduce the frame error rate, which can effectively improve the space utilization. The simulation results show that when the depth factor increment is appropriate and the frame error rate is 10−5, compared with the standard stack bucket algorithm, the frame error performance of the proposed algorithm is improved by about 0.6 dB, and the time complexity can be improved by 72.63%.
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