基于PRP共轭梯度法求解代价函数的RSC码参数识别算法

doi:10.12305/j.issn.1001-506X.2024.11.35

摘要/Abstract

摘要：

Turbo码是一种常用的信道编码方式, 正确识别Turbo码首先要正确识别其子递归系统卷积(recursive system convolutional, RSC)码, 由于信道噪声与干扰引发误码, 这就要求识别算法具有良好的抗误码性能以及识别能力。利用解调软判决序列, 通过编码码元约束方程, 构建指数形式的代价函数模型, 将识别RSC码的生成矩阵问题转化为求解代价函数全域极值的最优化问题, 最后在共轭梯度法的基础上, 采用新的PRP步长因子来寻找全域极值点。仿真结果表明, 所提算法与现有算法相比, 收敛速度更快, 在低信噪比下也有良好的识别能力。

关键词: 递归系统卷积码, 盲识别, 解调软判决, 共轭梯度法, 全域极值点

Abstract:

Turbo code is an common communication coding method. To correctly identify Turbo code, first of all, correct identification of its subcode recursive system convolutional (RSC) code is required. Due to the existence of channel noise and interference which leads to erroneous bits, this demands the identification algorithm having good error resilience performance and recognition ability. The demodulation soft judgment sequence is utilized to construct an exponential cost function model by encoding the symbol constraint equation. The problem of identifying the generator matrix of the RSC code is transformed into the optimization problem of solving the global extremum of the cost function. Finally, based on the conjugate gradient method, a new PRP step size factor is proposed to find the global extremum point. According to the simulation results, the proposed algorithm has faster rate of convergence and better recongnition ability at low signal-to-noise ratio than exisiting algorithms.

Key words: recursive system convolutional (RSC) code, blind identification, demodulation soft judgment, conjugate gradient method, global extremum point

中图分类号:

TN911.7

陈增茂, 李东豪, 孙溶辰, 孙志国. 基于PRP共轭梯度法求解代价函数的RSC码参数识别算法[J]. 系统工程与电子技术, 2024, 46(11): 3930-3937.

Zengmao CHEN, Donghao LI, Rongchen SUN, Zhiguo SUN. Parameter identification algorithm of RSC codes with solving cost function based on PRP conjugate gradient method[J]. Systems Engineering and Electronics, 2024, 46(11): 3930-3937.

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m	生成多项式
2	g₀(D)=1+D+D², g₁(D)=1+D²
3	g₀(D)=1+D+D³, g₁(D)=1+D+D²+D³
4	g₀(D)=1+D³+D⁴, g₁(D)=1+D+D²+D⁴
5	g₀(D)=1+D+D²+D³+D⁵, g₁(D)=1+D²+D⁴+D⁵
6	g₁(D)=1+D+D²+D³+D⁴+D⁶, g₀(D)=1+D²+D³+D⁵+D⁶
7	g₁(D)=1+D+D²+D³+D⁴+D⁷, g₀(D)=1+D²+D³+D⁵+D⁶+D⁷
8	g₁(D)=1+D+D²+D³+D⁴+D⁸, g₀(D)=1+D²+D³+D⁵+D⁶+D⁸

码率	生成多项式
1/2	g₀(D)=1+D+D²+D³+D⁴+D⁶, g₁(D)=1+D²+D³+D⁵+D⁶
1/3	g₀(D)=1+D+D²+D³+D⁵+D⁶, g₁(D)=1+D+D³+D⁴+D⁶, g₂(D)=1+D²+D³+D⁴+D⁵
1/4	g₀(D)=1+D+D³+D⁴+D⁵+D⁶, g₁(D)=1+D²+D³+D⁴+D⁶, g₂(D)=1+D+D³+D⁴+D⁵, g₃(D)=1+D³+D⁵+D⁶

算法	加法复杂度	乘法复杂度
本文算法	O(nmαL)	O(nm²αL)
LS算法	O(n²m²αL)	O(n²m²αL)
MC算法	O(nmαL)	O(nm²αL)
WHT算法	O(N²)=O(2^2l)	-

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