基于记忆因子的连续相位调制信号最大似然调制识别

吴斌; 袁亚博; 汪勃

doi:10.11999/JEIT151445

基于记忆因子的连续相位调制信号最大似然调制识别

doi: 10.11999/JEIT151445

计量
- 文章访问数: 1623
- HTML全文浏览量: 195
- PDF下载量: 328
- 被引次数: 0
出版历程
- 收稿日期: 2015-12-17
- 修回日期: 2016-05-10
- 刊出日期: 2016-10-19

Maximum Likelihood Modulation Recognition for Continuous Phase Modulation Signals Using Memory Factor

摘要

摘要: 为解决有记忆非线性的连续相位调制(CPM)信号调制方式识别精度低的问题，该文提出一种基于记忆因子的CPM信号最大似然调制识别新方法。该方法定义具有时齐马尔科夫性的映射符号，通过计算其后验概率构造记忆因子，进一步结合CPM分解和EM算法，推导出时间可分离，信道参数可估计的CPM信号似然函数。该调制识别方法所需符号数目少，适用信噪比范围广，识别CPM信号种类多且精度高，对相位误差鲁棒性强。仿真结果证明，当符号数目为200，信噪比为0 dB，相位误差任意时，该方法对8种CPM信号的识别率可达95%以上。
- 信号处理 /
- 调制识别 /
- 连续相位调制 /
- 最大似然 /
- EM算法
Abstract: In order to solve the problem of low recognition accuracy of Continuous Phase Modulation (CPM) which is non-linear and with memory, a new maximum likelihood modulation recognition approach using memory factor is proposed in this paper. The approach defines the mapping symbol which has the time-homogeneous Markov property and generates the memory factor by calculating the posterior probability of the mapping symbol. Then, combining with the CPM decomposition and the EM algorithm, the time separable and channel parameter estimable likelihood function is deduced for CPM signals. The proposed approach has characters of low required symbol number, wide range of applicable SNR, large variety of recognizable CPM signals, high recognition accuracy and strong robustness to phase error. Simulation results show that the recognition rate of 8 kinds of CPM signals can reach more than 95% when the symbol number is 200, SNR is 0 dB and the phase error is arbitrary.
- Signal processing /
- Modulation recognition /
- Continuous Phase Modulation (CPM) /
- Maximum likelihood /
- EM algorithm

HTML全文

参考文献(20)

DOBRE O A, ABDI A, BAR-NESS Y, et al. Survey of automatic modulation classification techniques: classical approaches and new trends[J]. IET Communications,　2007, 1(2): 137-159. doi: 10.1049/iet-com:20050176.

BULEK B, OZDEMIR O, VARSHNEY P K, et al. Distributed maximum likelihood classification of linear modulations over nonidentical flat block-fading Gaussian channels[J]. IEEE Transactions on Wireless Communications, 2015, 14(2): 724-737. doi: 10.1109/TWC.2014.2359019.

ZHU Z C and NANDI A K. Blind digital modulation classification using minimum distance centroid estimator and non-parametric likelihood function[J]. IEEE Transactions on Wireless Communications, 2014, 13(8): 4483-4494. doi: 10.1109/TWC.2014.2320724.

张路平, 王建新. MQAM信号调制方式盲识别[J]. 电子与信息学报, 2011, 33(2): 332-336. doi: 10.3724/SP.J.1146.2010.00472.

ZHANG Luping and WANG Jianxin. Blind modulation recognition algorithm for MQAM signals[J]. Journal of Electronics Information Technology, 2011, 33(2): 332-336. doi: 10.3724/SP.J.1146.2010.00472.

廖灿辉, 涂世龙, 万坚. 一种抗频偏的卫星幅相调制信号识别算法[J]. 电子与信息学报, 2014, 36(2): 346-352. doi: 10.3724/SP.J.1146.2013.00512.

LIAO Canhui, TU Shilong, and WAN Jian. An anti-frequency-offset algorithm for modulation recognition of satellite amplitude-phase modulated signals[J]. Journal of Electronics Information Technology, 2014, 36(2): 346-352. doi: 10.3724/SP.J.1146.2013.00512.

刘明骞, 李兵兵, 曹超凤, 等. 认知无线电中非高斯噪声下数字调制信号识别方法[J]. 通信学报, 2014, 35(1): 82-88. doi: 10.3969/j.issn.1000-436x.2014.01.010.

LIU Mingqian, LI Bingbing, CAO Chaofeng, et al.. Recognition method of digital modulation signals over non-Gaussian noise in cognitive radio[J]. Journal on Communications, 2014, 35(1): 82-88. doi: 10.3969/j.issn. 1000-436x.2014.01.010.

LI B, BAI B, AULIN T, et al. Advanced continuous phase modulation for high mobility communications[J]. Chinese Science Bulletin, 2014, 59(35): 4999-5010. doi: 10.1007/ s11434-014-0595-9.

REMLEIN P. Energy efficient continuous phase modulation signals for satellite intelligent transportation systems[J]. IET Circuits, Devices Systems, 2014, 8(5): 406-411. doi: 10.1049/iet-cds.2013.0433.

钟凯, 彭华, 葛临东. 基于FABA-SISO的时变频率选择性衰落信道CPM信号盲均衡[J]. 电子与信息学报, 2015, 37(11): 2672-2677. doi: 10.11999/JEIT150026.

ZHONG Kai, PENG Hua, and GE Lindong. Blind equalization based on FABA-SISO for continuous phase modulation signals over time-varying frequency-selective fading channel[J]. Journal of Electronics Information Technology, 2015, 37(11): 2672-2677. doi: 10.11999/JEIT150026.

PAWAR S U and DOHERTY J F. Modulation recognition in continuous phase modulation using approximate entropy[J]. IEEE Transactions on Information Forensics Security, 2011, 6(3): 843-852. doi: 10.1109/TIFS.2011.2159000.

BARI M and DOROSLOVACKI M. Simple features for separating CPFSK from QAM and PSK modulations[J]. IEEE Signal Processing Letters, 2015, 22(5): 613-617. doi: 10.1109/LSP.2014.2363615.

BOUTTE D and SANTHANAM B. A hybrid ICA-SVM approach to continuous phase modulation recognition[J]. IEEE Signal Processing Letters, 2009, 16(5): 402-405. doi: 10.1109/LSP.2009.2016444.

OZDEMIR O, LI R, and VARSHNEY P K. Hybrid maximum likelihood modulation classification using multiple radios[J]. IEEE Communications Letters, 2013, 17(10): 1889-1892. doi: 10.1109/LCOMM.2013.081913.131351.

LAURENT P A. Exact and approximate construction of digital phase modulations by superposition of amplitude modulated pulses (AMP)[J]. IEEE Transactions on Communications, 1986, 34(2): 150-160. doi: 10.1109/TCOM.1986.1096504.

TSU C P and CHANG D C. Modulation classification for M-ary CPM signals: based on PAM decomposition and phase detection methods[C]. IEEE International Conference on Computer Communications and Networks, Maui, 2011: 1-5. doi: 10.1109/ICCCN.2011.6005913.

ABDI H and WILLIAMS L J. Principal component analysis[J]. Wiley Interdisciplinary Reviews: Computational Statistics, 2010, 2(4): 433-459. doi: 10.1002/wics.101.

施引文献

资源附件(0)

访问统计