Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks
Cooperative automatic modulation classification (CAMC) using a swarm of sensors is intriguing nowadays as it would be much more robust than the conventional single-sensing-node automatic modulation classification (AMC) method. We propose a novel robust CAMC approach using vectorized soft decision fu...
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MDPI AG
2022-02-01
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| Online Access: | https://www.mdpi.com/1424-8220/22/5/1797 |
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| author | Xiao Yan Yan Zhang Xiaoxue Rao Qian Wang Hsiao-Chun Wu Yiyan Wu |
| author_facet | Xiao Yan Yan Zhang Xiaoxue Rao Qian Wang Hsiao-Chun Wu Yiyan Wu |
| author_sort | Xiao Yan |
| collection | DOAJ |
| description | Cooperative automatic modulation classification (CAMC) using a swarm of sensors is intriguing nowadays as it would be much more robust than the conventional single-sensing-node automatic modulation classification (AMC) method. We propose a novel robust CAMC approach using vectorized soft decision fusion in this work. In each sensing node, the local Hamming distances between the graph features acquired from the unknown target signal and the training modulation candidate signals are calculated and transmitted to the fusion center (FC). Then, the global CAMC decision is made by the indirect vote which is translated from each sensing node’s Hamming-distance sequence. The simulation results demonstrate that, when the signal-to-noise ratio (SNR) was given by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>η</mi></semantics></math></inline-formula> ≥ <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mspace width="3.33333pt"></mspace><mrow><mi>dB</mi></mrow></mrow></semantics></math></inline-formula>, our proposed new CAMC scheme’s correct classification probability <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>P</mi><mrow><mi>cc</mi></mrow></msub></semantics></math></inline-formula> could reach up close to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>100</mn><mo>%</mo></mrow></semantics></math></inline-formula>. On the other hand, our proposed new CAMC scheme could significantly outperform the single-node graph-based AMC technique and the existing decision-level CAMC method in terms of recognition accuracy, especially in the low-SNR regime. |
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| spelling | doaj.art-ec6421bf8a8e48ce86dba3c384a9452d2023-11-23T23:46:20ZengMDPI AGSensors1424-82202022-02-01225179710.3390/s22051797Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor NetworksXiao Yan0Yan Zhang1Xiaoxue Rao2Qian Wang3Hsiao-Chun Wu4Yiyan Wu5School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA 70803, USACommunications Research Centre, Ottawa, ON K2H 8S2, CanadaCooperative automatic modulation classification (CAMC) using a swarm of sensors is intriguing nowadays as it would be much more robust than the conventional single-sensing-node automatic modulation classification (AMC) method. We propose a novel robust CAMC approach using vectorized soft decision fusion in this work. In each sensing node, the local Hamming distances between the graph features acquired from the unknown target signal and the training modulation candidate signals are calculated and transmitted to the fusion center (FC). Then, the global CAMC decision is made by the indirect vote which is translated from each sensing node’s Hamming-distance sequence. The simulation results demonstrate that, when the signal-to-noise ratio (SNR) was given by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>η</mi></semantics></math></inline-formula> ≥ <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mspace width="3.33333pt"></mspace><mrow><mi>dB</mi></mrow></mrow></semantics></math></inline-formula>, our proposed new CAMC scheme’s correct classification probability <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>P</mi><mrow><mi>cc</mi></mrow></msub></semantics></math></inline-formula> could reach up close to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>100</mn><mo>%</mo></mrow></semantics></math></inline-formula>. On the other hand, our proposed new CAMC scheme could significantly outperform the single-node graph-based AMC technique and the existing decision-level CAMC method in terms of recognition accuracy, especially in the low-SNR regime.https://www.mdpi.com/1424-8220/22/5/1797cooperative automatic modulation classification (CAMC)vectorized decision metricssoft-decision-level fusiongraph-based automatic modulation classificationHamming distance sequence |
| spellingShingle | Xiao Yan Yan Zhang Xiaoxue Rao Qian Wang Hsiao-Chun Wu Yiyan Wu Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks Sensors cooperative automatic modulation classification (CAMC) vectorized decision metrics soft-decision-level fusion graph-based automatic modulation classification Hamming distance sequence |
| title | Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks |
| title_full | Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks |
| title_fullStr | Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks |
| title_full_unstemmed | Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks |
| title_short | Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks |
| title_sort | novel cooperative automatic modulation classification using vectorized soft decision fusion for wireless sensor networks |
| topic | cooperative automatic modulation classification (CAMC) vectorized decision metrics soft-decision-level fusion graph-based automatic modulation classification Hamming distance sequence |
| url | https://www.mdpi.com/1424-8220/22/5/1797 |
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