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复杂系统与复杂性科学  2018, Vol. 15 Issue (1): 68-74    DOI: 10.13306/j.1672-3813.2018.01.010
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阈值阵列系统中TCM编译码的随机共振现象
李恒, 王友国, 翟其清
南京邮电大学 通信与信息工程学院,南京 210003
Stochastic Resonance of TCM Coding and Decoding in Threshold Array Systems
LI Heng, WANG Youguo, ZHAI Qiqing
College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing Jiangsu 210003, China
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摘要 为了进一步降低网格编码调制(TCM)信号在接收端的误码率(BER),提高TCM码的译码性能。采用了一种离散的多阈值阵列系统与维特比译码器相结合的系统。并经过理论推导出阈值阵列系统输出端信号和TCM编码信号之间的互信息;同时通过仿真实验,分析了误码率的变化情况;并对两种不同测度下的变化情况进行了对比。理论分析表明,在适当噪声条件下,使信号无损传输到译码端;仿真实验也表明,在适当的噪声强度阈值阵列单元数量和噪声强度条件下,误码率会得到大幅度的降低。对比两种测度下的随机共振现象(SR),发现随机共振的存在性与测度有关。理论分析和仿真实验都表明,在该系统中适当的噪声能够显著提高互信息,降低误码率;随着阈值单元数的增加,这种效果也越发明显。
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李恒
王友国
翟其清
关键词 随机共振多阈值阵列系统互信息误码率TCM编码    
Abstract:In order to further reduce the bit error rate (BER) of the trellis coded modulation (TCM) signal at the receiving end, the decoding performance of the TCM code is improved, A discrete multi-threshold array system combined with a Viterbi decoder is used. The mutual information between the output signal of the threshold array system and the TCM coded signal is deduced through theory. At the same time, the variation of the BER is analyzed through simulation experiments. The changes under two different measures are compared. Theoretical analysis shows that under proper noise conditions, the signal is transmitted losslessly to the decoding end; Simulation experiments also show that BER will be greatly reduced under the appropriate number of noise intensity threshold array elements and noise intensity. Comparing the stochastic resonance (SR) under two measures, we found that the existence of stochastic resonance is related to the measurement. Theoretical analysis and simulation experiments show that proper noise in this system can significantly improve mutual information and reduce BER.As the number of threshold cells increases, this effect becomes more pronounced.
Key wordsStochastic Resonance    multi-threshold array system    mutual information    bit error rate    Trellis Coded Modulation
收稿日期: 2017-11-27      出版日期: 2019-01-10
ZTFLH:  TN911.4  
基金资助:国家自然科学基金(61179027);江苏省“青蓝工程”基金(QL06212006)
通讯作者: 王友国(1968),男,江苏淮安人,教授,博士,主要研究方向为信号与信息处理、压缩感知、社交网络分析。   
作者简介: 李恒(1995),男,安徽淮北人,硕士研究生,主要研究方向为信号处理及其应用技术。
引用本文:   
李恒, 王友国, 翟其清. 阈值阵列系统中TCM编译码的随机共振现象[J]. 复杂系统与复杂性科学, 2018, 15(1): 68-74.
LI Heng, WANG Youguo, ZHAI Qiqing. Stochastic Resonance of TCM Coding and Decoding in Threshold Array Systems. Complex Systems and Complexity Science, 2018, 15(1): 68-74.
链接本文:  
http://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2018.01.010      或      http://fzkx.qdu.edu.cn/CN/Y2018/V15/I1/68
[1]Benzi R, Sutera A. Vulpiani A. The mechanism of stochastic resonance [J]. Journal of Physics A: Mathematical and General, 1981, 14(11): L453L457.
[2]Benzi R, Parisi G, Sutera A, et al. A theory of stochastic resonance in climatic change [J]. SIAM Journal on Applied Mathematics, 1983, 43(3): 565578.
[3]Gammaitoni L, Hänggi p, Jung P, et al. Stochastic resonance [J]. Reviews of Modern Physics, 1998, 70(1): 45105.
[4]祁明,许丽艳,季冰, 等. 周期性语音信号传输的超阈值随机共振研究[J]. 复杂系统与复杂性科学, 2013, 10(3) : 3136.
Qi Ming, Xu Liyan, Ji Bing, et al. Supra-threshold stochastic resonance phenomenon of periodic voice signal transmission [J]. Complex Systems and Complexity Science, 2013, 10(3): 3136.
[5]方鸿雁,潘园园,孙华通, 等. 耦合神经网络中脉冲信号传输的噪声增强研究[J]. 复杂系统与复杂性科学, 2017, 14(2): 5964.
Fang Hongyan, Pan Yuanyuan, Sun Huatong, et al. Study of noise-enhanced pulse signal transmission in coupling neural networks [J]. Complex Systems and Complexity Science, 2017, 14(2): 5964.
[6]李欢,王友国. 一类非线性神经网络中噪声改善信息传输[J]. 物理学报, 2014, 63(12): 6369.
Li Huan, Wang Youguo. Noise-enhanced information transmission of a non-linear multilevel threshold neural networks system [J]. Acta Physica Sinica, 2014, 63(12): 6369.
[7]郭永峰,谭建国. 一类非线性神经网络系统的超阈值随机共振现象[J]. 物理学报, 2012, 61(17): 5559.
Guo Yongfeng, Tan Jianguo. Suprathreshold stochastic resonance of a non-linear multilevel threshold neuronal networks system [J]. Acta Physica Sinica, 2012, 61(17): 5559.
[8]Duan F, Chapeau-blondeau F, Abbott D. Double-maximum enhancement of signal-to-noise ratio gain via stochastic resonance and vibrational resonance [J]. Physical Review E, 2014, 90(2): 022134.
[9]张礁石,杨子贤,卢结成. 阈值阵列模型下的超阈值随机共振信噪比增益[J]. 数据采集与处理, 2013, 28(2): 226230.
Zhang Jiaoshi, Yang Zixian, Lu Jiecheng. Signal-to-noise ratio gain of suprathreshold stochastic resonance based on threshold array model [J]. Journal of Data Acquisition & Processing, 2013, 28(2): 226230.
[10] Duan F, Abbott D. Binary modulated signal detection in a bistable receiver with stochastic resonance [J]. Physica A: Statistical Mechanics and its Applications, 2007, 376: 173190.
[11] Liu J, Li Z, Guan L, et al. A novel parameter-tuned stochastic resonator for binary PAM signal processing at low SNR [J]. IEEE Communications Letters, 2014, 18(3): 427430.
[12] 王爱珍,侯成郭,任国凤. 直接序列扩频的分层级联随机共振接收算法[J]. 计算机应用, 2015, 35(4): 934937,959.
Wang Aizhen, Hou Chengguo, Ren Guofeng. Layered and cascaded stochastic resonance algorithm for direct sequence spread spectrum signal receiving [J]. Journal of Computer Applications, 2015, 35(4): 934937,959.
[13] Sugiura S, Ichiki A, Tadokoro Y. Stochastic-resonance based iterative detection for serially-concatenated turbo codes [J]. IEEE Signal Processing Letters, 2012, 19(10): 655658.
[14] Zhai Q Q, Wang Y G. Stochastic resonance in parallel concatenated turbo code decoding [J]. Digital Signal Processing, 2016, 56:9399.
[15] Ungerboeck G. Channel coding with multilevel/phase signals [J]. IEEE Transactions on Information Theory, 1982, 28(1): 5567.
[16] Ungerboeck G. Trellis-coded modulation with redundant signal sets Part II: State of the art [J]. IEEE Communications Magazine, 1987, 25(2): 1221.
[17] Mcdonnell M D, Gao X. M-ary suprathreshold stochastic resonance: generalization and scaling beyond binary threshold nonlinearities [J]. EPL, 2015, 108(6): 60003.
[18] 曹雪虹,张宗橙. 信息论与编码[M]. 北京: 清华大学出版社, 2004: 1016.
[19] Viterbi A. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm [J]. IEEE Transactions on Information Theory, 1967, 13(2): 260269.
[20] Das A, Stocks N G, Nikitin A, et al. Quantifying stochastic resonance in a single threshold detector for random aperiodic signals [J]. Fluctuation and Noise Letters, 2004, 4(2): L247L265.
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