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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