Reed-Solomon码概率软译码算法增强技术

Enhanced stochastic soft decoding algorithm for Reed-Solomon codes

基于概率计算的Chase算法可以在保证译码性能的情况下,大幅度降低软译码的复杂度,使得RS软译码算法得到实际应用.然而,概率Chase算法的时间复杂度会随着测试码字个数的增加而增长;在高阶调制下, SSCA算法产生测试码字时的搜索范围会随着调制阶数的增加呈指数增长,从而增大了硬件存储开销.为降低时间复杂度,本文提出了提前输出算法,不需要产生所有的测试码字即可实现成功译码.仿真结果表明该算法可以在逼近原算法误帧率性能的同时将时间复杂度最多降低为原来的近1/τ(τ为测试码字个数).针对SSCA算法存储的压力,本文通过确定搜索半径辅助的选择方法来减小搜索范围.仿真结果表明,提出的3σ-SSCA算法仅以少量性能损失为代价可将测试码字单个符号的搜索范围由q (q为调制阶数)个缩小为个位数.

The Chase algorithm based on stochastic computing can significantly reduce the complexity of soft decoding while ensuring the decoding performance, so that the soft decoding algorithm of Reed-Solomon codes is practically applied. However, the time complexity of the stochastic Chase algorithm will increase as the number of test test-vectors increases. In addition, under high-order modulation, the search range of generating test-vectors in the symbol-level stochastic Chase algorithm(SSCA) will increase exponentially by the order of modulation,thus increasing the hardware storage consumption. In order to reduce the time complexity, this paper proposes an early output algorithm, which does not require all test-vectors to achieve successful decoding. Simulation results show that the proposed algorithm can approach the original stochastic decoding performance while reducing the time complexity to nearly 1/τ(τ is the number of test-vectors). In view of the storage issue in the SSCA,this paper reduces the search range by search-radius-determination assisted selection method. Simulation results indicate that the proposed 3σ-SSCA can reduce the search range of a single symbol of a test-vector from q(q is the order of modulation) to less than ten at the cost of a small performance loss.