基于ICA和特征提取的MIMO信號(hào)調(diào)制識(shí)別算法

張?zhí)祢U; 范聰聰; 葛宛營(yíng); 張?zhí)?/>
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doi:10.11999/JEIT190320

基于ICA和特征提取的MIMO信號(hào)調(diào)制識(shí)別算法

doi: 10.11999/JEIT190320

重慶郵電大學(xué)通信與信息工程學(xué)院重慶 400065

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61671095, 61702065, 61701067, 61771085)，信號(hào)與信息處理重慶市市級(jí)重點(diǎn)實(shí)驗(yàn)室建設(shè)項(xiàng)目(CSTC2009CA2003)，重慶市研究生科研創(chuàng)新項(xiàng)目(CYS17219)，重慶市教育委員會(huì)科研項(xiàng)目(KJ1600427, KJ1600429)

詳細(xì)信息

作者簡(jiǎn)介:
張?zhí)祢U：男，1971年生，博士后，教授，主要研究方向?yàn)檎Z(yǔ)音信號(hào)處理、通信信號(hào)的調(diào)制解調(diào)、盲處理、神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)以及FPGA, VLSI 實(shí)現(xiàn)

范聰聰：男，1995年生，碩士生，研究方向?yàn)橥ㄐ判盘?hào)的盲處理

葛宛營(yíng)：男，1994年生，碩士生，研究方向?yàn)檎Z(yǔ)音增強(qiáng)與盲分離

張?zhí)欤耗校?993年生，碩士生，研究方向?yàn)檎Z(yǔ)音信號(hào)處理，聲樂(lè)分離

通訊作者:
范聰聰　2669432120@qq.com

中圖分類號(hào): TN911.7
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出版歷程
- 收稿日期: 2019-05-06
- 修回日期: 2020-03-18
- 網(wǎng)絡(luò)出版日期: 2020-06-26
- 刊出日期: 2020-09-27

MIMO Signal Modulation Recognition Algorithm Based on ICA and Feature Extraction

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61671095, 61702065, 61701067, 61771085), The Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009CA2003), The Chongqing Graduate Research and Innovation Project (CYS17219), The Research Project of Chongqing Educational Commission (KJ1600427, KJ1600429)

摘要

摘要: 針對(duì)非協(xié)作通信中多輸入多輸出(MIMO)信號(hào)的盲調(diào)制識(shí)別，該文提出一種基于獨(dú)立分量分析(ICA)和特征提取的調(diào)制識(shí)別算法。根據(jù)空分復(fù)用MIMO系統(tǒng)各發(fā)送天線上信號(hào)的獨(dú)立性，利用ICA算法從接收的混合信號(hào)中分離出發(fā)射信號(hào)。為實(shí)現(xiàn)全盲條件下的調(diào)制識(shí)別，在進(jìn)行ICA分離前，利用最小描述長(zhǎng)度(MDL)準(zhǔn)則估計(jì)發(fā)射天線數(shù)。在得到發(fā)射信號(hào)之后，首先利用6階累積量、循環(huán)譜和4次方譜算法構(gòu)造4個(gè)特征參數(shù)，然后利用分層結(jié)構(gòu)的神經(jīng)網(wǎng)絡(luò)分類器識(shí)別信號(hào)的調(diào)制類型。仿真結(jié)果表明，所提方法可在較低信噪比下對(duì){2PSK, 2ASK, 2FSK, 4PSK, 4ASK, MSK, 8PSK, 16QAM}8種MIMO信號(hào)進(jìn)行有效識(shí)別，當(dāng)發(fā)送天線數(shù)為2、接收天線數(shù)為5、信噪比為2 dB時(shí)，識(shí)別率可達(dá)到98%以上。
- 信號(hào)處理 /
- 多輸入多輸出信號(hào) /
- 獨(dú)立分量分析 /
- 6階累積量 /
- 循環(huán)譜 /
- 神經(jīng)網(wǎng)絡(luò)
Abstract: For blind modulation recognition of Multiple Input Multiple Output (MIMO) signals in non-cooperative communication, a modulation recognition method based on Independent Component Analysis (ICA) and feature extraction is proposed. According to the signal independence of each transmitting antenna in space division multiplexing MIMO system, the ICA algorithm is used to separate the transmitting signal from the received mixed signal. In order to realize modulation recognition under completely blind condition, the Minimum Description Length (MDL) criterion is used to estimate the number of transmitting antennas before ICA separation. After obtaining the transmitted signal, four characteristic parameters are constructed by using six-order cumulant, cyclic spectrum and fourth-power spectrum algorithm, and then the modulation type of the signal is identified by using hierarchical neural network classifier. The simulation results show that the proposed method can effectively recognize {2PSK, 2ASK, 2FSK, 4PSK, 4ASK, MSK, 8PSK, 16QAM} eight MIMO signals at low SNR. When the number of transmitting antennas is 2, the number of receiving antennas is 5 and the SNR is 2dB, the recognition rate can reach more than 98%.
- Signal processing /
- Multiple Input Multiple Output (MIMO) signals /
- Independent Component Analysis (ICA) /
- Sixth-order cumulant /
- Cyclic spectrum /
- Neural network

HTML全文

圖 1 分層結(jié)構(gòu)的神經(jīng)網(wǎng)絡(luò)分類器

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圖 2 調(diào)制識(shí)別流程圖

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圖 3 不同 ${N_{\rm{t}}} \times {N_{\rm{r}}}$ 下的估計(jì)性能

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圖 4 不同調(diào)制方式下 ${N_{\rm{t}}}$ 的估計(jì)性能

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圖 5 特征參數(shù)T₁隨SNR的變化曲線

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圖 6 特征參數(shù)T₂隨SNR的變化曲線

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圖 7 特征參數(shù)T₃隨SNR的變化曲線

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圖 8 特征參數(shù)T₄隨SNR的變化曲線

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表 1 各調(diào)制信號(hào)的高階累積量

累積量	2PSK	4PSK	8PSK	2ASK	4ASK	2FSK	MSK	16QAM
$\left\| {{C_{21}}} \right\|$	1	1	1	1	1	1	1	1
$\left\| {{C_{42}}} \right\|$	2	1	1	2	1.36	1	1	0.68
$\left\| {{C_{63}}} \right\|$	13	4	4	13	9.16	4	4	2.08

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表 2 ${N_{\rm{t}}} \times {N_{\rm{r}}} = 4 \times 5$ 時(shí)的識(shí)別率(%)

調(diào)制類型	–2 dB	0 dB	2 dB	4 dB	6 dB	8 dB	10 dB
2PSK	80.6	85.7	89.5	94.0	97.0	98.8	100
2ASK	82.4	87.3	92.0	95.2	97.0	99.3	100
2FSK	89.0	93.0	94.8	97.3	100	100	100
4PSK	89.4	93.0	96.9	98.2	98.8	100	100
4ASK	85.0	87.0	90.2	96.0	100	100	100
MSK	88.5	92.4	96.0	98.3	99.5	100	100
8PSK	91.0	95.0	97.8	99.0	100	100	100
16QAM	87.0	92.3	95.6	98.2	100	100	100

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表 3 ${N_{\rm{t}}} \times {N_{\rm{r}}} = 4 \times 6$ 時(shí)的識(shí)別率(%)

調(diào)制類型	-2 dB	0 dB	2 dB	4 dB	6 dB	8 dB	10 dB
2PSK	83.7	88.0	95.0	98.2	99.4	100	100
2ASK	84.5	90.3	96.0	98.4	100	100	100
2FSK	91.3	96.0	98.2	99.5	100	100	100
4PSK	91.6	96.4	97.5	100	100	100	100
4ASK	89.0	94.4	98.0	99.2	100	100	100
MSK	91.0	95.0	97.3	100	100	100	100
8PSK	93.0	97.2	99.3	100	100	100	100
16QAM	91.0	95.3	98.4	100	100	100	100

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表 4 ${N_{\rm{t}}} \times {N_{\rm{r}}}{\rm{ = 2}} \times {\rm{5}}$ 時(shí)的識(shí)別率(%)

調(diào)制類型	-2 dB	0 dB	2 dB	4 dB	6 dB	8 dB	10 dB
2PSK	89.0	93.1	98.3	99.0	100	100	100
2ASK	91.6	95.0	98.2	99.0	100	100	100
2FSK	93.4	98.3	99.5	100	100	100	100
4PSK	95.8	98.3	100	100	100	100	100
4ASK	92.0	97.0	99.6	100	100	100	100
MSK	94.0	97.6	98.8	100	100	100	100
8PSK	96.8	99.3	100	100	100	100	100
16QAM	94.3	98.0	100	100	100	100	100

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參考文獻(xiàn)(16)

ZHAO Yong, XU Yitao, JIANG Han, et al. Recognition of digital modulation signals based on high-order cumulants[C]. 2015 International Conference on Wireless Communications & Signal Processing, Nanjing, China, 2015: 1–5. doi: 10.1109/WCSP.2015.7341279.

郭立民, 寇韻涵, 陳濤, 等. 基于棧式稀疏自編碼器的低信噪比下低截獲概率雷達(dá)信號(hào)調(diào)制類型識(shí)別[J]. 電子與信息學(xué)報(bào), 2018, 40(4): 875–881. doi: 10.11999/JEIT170588

GUO Limin, KOU Yunhan, CHEN Tao, et al. Low probability of intercept radar signal recognition based on stacked sparse auto-encoder[J]. Journal of Electronics &Information Technology, 2018, 40(4): 875–881. doi: 10.11999/JEIT170588

陳濤, 柳立志, 郭立民. 基于MWC壓縮采樣寬帶接收機(jī)的雷達(dá)信號(hào)脈內(nèi)調(diào)制識(shí)別[J]. 電子與信息學(xué)報(bào), 2018, 40(4): 867–874. doi: 10.11999/JEIT170612

CHEN Tao, LIU Lizhi, and GUO Limin. Intra-pulse modulation recognition of radar signals based on MWC compressed sampling wideband receiver[J]. Journal of Electronics &Information Technology, 2018, 40(4): 867–874. doi: 10.11999/JEIT170612

張利, 李青. 基于高階累積量的調(diào)制識(shí)別算法的研究[J]. 信息工程大學(xué)學(xué)報(bào), 2017, 18(4): 403–408. doi: 10.3969/j.issn.1671-0673.2017.04.005

ZHANG Li and LI Qing. Research on modulation recognition algorithm based on higher-order cumulant[J]. Journal of Information Engineering University, 2017, 18(4): 403–408. doi: 10.3969/j.issn.1671-0673.2017.04.005

譚曉衡, 褚國(guó)星, 張雪靜, 等. 基于高階累積量和小波變換的調(diào)制識(shí)別算法[J]. 系統(tǒng)工程與電子技術(shù), 2018, 40(1): 171–177. doi: 10.3969/j.issn.1001-506X.2018.01.25

TAN Xiaoheng, CHU Guoxing, ZHANG Xuejing, et al. Modulation recognition algorithm based on high - order cumulants and wavelet transform[J]. Systems Engineering and Electronics, 2018, 40(1): 171–177. doi: 10.3969/j.issn.1001-506X.2018.01.25

趙雄文, 郭春霞, 李景春. 基于高階累積量和循環(huán)譜的信號(hào)調(diào)制方式混合識(shí)別算法[J]. 電子與信息學(xué)報(bào), 2016, 38(3): 674–680. doi: 10.11999/JEIT150747

ZHAO Xiongwen, GUO Chunxia, and LI Jingchun. Mixed recognition algorithm for signal modulation schemes by high-order cumulants and cyclic spectrum[J]. Journal of Electronics &Information Technology, 2016, 38(3): 674–680. doi: 10.11999/JEIT150747

CHOQUEUSE V, AZOU S, YAO K, et al. Blind modulation recognition for MIMO systems[J]. MTA Review, 2009, 19(2): 183–196.

張路平, 王建新. MIMO信號(hào)調(diào)制方式盲識(shí)別[J]. 應(yīng)用科學(xué)學(xué)報(bào), 2012, 30(2): 135–140. doi: 10.3969/j.issn.0255-8297.2012.02.005

ZHANG Luping and WANG Jianxin. Blind modulation recognition for MIMO signals[J]. Journal of Applied Sciences, 2012, 30(2): 135–140. doi: 10.3969/j.issn.0255-8297.2012.02.005

DAS D, BORA P K, and BHATTACHARJEE R. Blind modulation recognition of the lower order PSK signals under the MIMO Keyhole channel[J]. IEEE Communications Letters, 2018, 22(9): 1834–1837. doi: 10.1109/LCOMM.2018.2853638

WEI Mengchuan, WEI Zaixue, YANG Jianyi, et al. Automatic modulation recognition of digital signal based on auto-encoding network in MIMO System[C]. The 18th IEEE International Conference on Communication Technology, Chongqing, China, 2018: 1017–1021. doi: 10.1109/ICCT.2018.8600148.

LIU Xiaokai, ZHAO Chenglin, WANG Pengbiao, et al. Blind modulation classification algorithm based on machine learning for spatially correlated MIMO system[J]. IET Communications, 2017, 11(7): 1000–1007. doi: 10.1049/iet-com.2015.1222

COMON P and JUTTEN C. Handbook of Blind Source Separation: Independent Component Analysis and Applications[M]. Oxford: Academic Press, 2010: 147–153.

許宏吉, 劉琚, 谷波, 等. 空時(shí)分組碼通信中的一類ICA盲檢測(cè)方案[J]. 通信學(xué)報(bào), 2007, 28(6): 12–19. doi: 10.3321/j.issn:1000-436X.2007.06.003

XU Hongji, LIU Ju, GU Bo, et al. ICA based blind detection scheme in space-time block coding communications[J]. Journal on Communications, 2007, 28(6): 12–19. doi: 10.3321/j.issn:1000-436X.2007.06.003

WANG Rui. Research on digital signal modulation recognition and parameter estimation based on cyclostationarity[D]. [Master dissertation], University of Electronic Science and Technology, 2012: 20–24.

REYNALDI A, LUKAS S, and MARGARETHA H. Backpropagation and Levenberg-Marquardt algorithm for training finite element neural network[C]. The 6th UKSim/AMSS European Symposium on Computer Modeling and Simulation, Valetta, Malta, 2012: 89–94. doi: 10.1109/EMS.2012.56.

KHOSRAVIANI M, KALBKHANI H, and SHAYESTEH M G. Digital modulation recognition in MIMO systems based on segmentation of received data[C]. 2017 Iranian Conference on Electrical Engineering, Tehran, Iran, 2017: 1998–2002. doi: 10.1109/IranianCEE.2017.7985384.

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