Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics
The cognitive radio (CR) network consists of primary users (PUs) and secondary users (SUs). The SUs in the CR network senses the spectrum band to opportunistically access the white space. Exploiting the white spaces helps to improve the spectrum efficiency. Owing to the excellent learning ability of...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9096319/ |
| _version_ | 1818330205755801600 |
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| author | Brijesh Soni Dhaval K. Patel Miguel Lopez-Benitez |
| author_facet | Brijesh Soni Dhaval K. Patel Miguel Lopez-Benitez |
| author_sort | Brijesh Soni |
| collection | DOAJ |
| description | The cognitive radio (CR) network consists of primary users (PUs) and secondary users (SUs). The SUs in the CR network senses the spectrum band to opportunistically access the white space. Exploiting the white spaces helps to improve the spectrum efficiency. Owing to the excellent learning ability of machine learning/deep learning framework, many works in the recent past have applied shallow/deep multi-layer perceptron approach for spectrum sensing. However, the multi-layer perceptron networks are not well suited for time-series data due to the absence of memory elements. On the other hand, long short-term memory (LSTM) network, an improved version of Recurrent neural network is well suited for time-series data. In this paper, we propose an LSTM based spectrum sensing (LSTM-SS), which learns the implicit features from the spectrum data, for instance, the temporal correlation (i.e., the correlation between the present and past timestamp).Moreover, the CR systems also exploits the PU activity statistics to improve the CR performance. In this context, we compute the PU activity statistics like on and off period duration, duty cycle and propose the PU activity statistics based spectrum sensing (PAS-SS) to enhance the sensing performance. The proposed sensing schemes are validated on the spectrum data of various radio technologies acquired using an experimental test-bed setup. The proposed LSTM-SS scheme is compared with the state of the art spectrum sensing techniques. Experimental results indicate that the proposed schemes has improved detection performance and classification accuracy at low signal to noise ratio regimes. We notice that the improvement achieved is at the cost of longer training time and a nominal increase in execution time. |
| first_indexed | 2024-12-13T13:00:15Z |
| format | Article |
| id | doaj.art-1836ae4d61c640478e70c9f26111fd4b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:00:15Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1836ae4d61c640478e70c9f26111fd4b2022-12-21T23:45:03ZengIEEEIEEE Access2169-35362020-01-018974379745110.1109/ACCESS.2020.29956339096319Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity StatisticsBrijesh Soni0https://orcid.org/0000-0002-0362-5087Dhaval K. Patel1https://orcid.org/0000-0002-1350-4959Miguel Lopez-Benitez2https://orcid.org/0000-0003-0526-6687School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, IndiaSchool of Engineering and Applied Science, Ahmedabad University, Ahmedabad, IndiaDepartment of Electrical Engineering and Electronics, University of Liverpool, Liverpool, U.K.The cognitive radio (CR) network consists of primary users (PUs) and secondary users (SUs). The SUs in the CR network senses the spectrum band to opportunistically access the white space. Exploiting the white spaces helps to improve the spectrum efficiency. Owing to the excellent learning ability of machine learning/deep learning framework, many works in the recent past have applied shallow/deep multi-layer perceptron approach for spectrum sensing. However, the multi-layer perceptron networks are not well suited for time-series data due to the absence of memory elements. On the other hand, long short-term memory (LSTM) network, an improved version of Recurrent neural network is well suited for time-series data. In this paper, we propose an LSTM based spectrum sensing (LSTM-SS), which learns the implicit features from the spectrum data, for instance, the temporal correlation (i.e., the correlation between the present and past timestamp).Moreover, the CR systems also exploits the PU activity statistics to improve the CR performance. In this context, we compute the PU activity statistics like on and off period duration, duty cycle and propose the PU activity statistics based spectrum sensing (PAS-SS) to enhance the sensing performance. The proposed sensing schemes are validated on the spectrum data of various radio technologies acquired using an experimental test-bed setup. The proposed LSTM-SS scheme is compared with the state of the art spectrum sensing techniques. Experimental results indicate that the proposed schemes has improved detection performance and classification accuracy at low signal to noise ratio regimes. We notice that the improvement achieved is at the cost of longer training time and a nominal increase in execution time.https://ieeexplore.ieee.org/document/9096319/Cognitive radiospectrum sensinglong short-term memoryprimary user activity statisticsdeep learning |
| spellingShingle | Brijesh Soni Dhaval K. Patel Miguel Lopez-Benitez Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics IEEE Access Cognitive radio spectrum sensing long short-term memory primary user activity statistics deep learning |
| title | Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics |
| title_full | Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics |
| title_fullStr | Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics |
| title_full_unstemmed | Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics |
| title_short | Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics |
| title_sort | long short term memory based spectrum sensing scheme for cognitive radio using primary activity statistics |
| topic | Cognitive radio spectrum sensing long short-term memory primary user activity statistics deep learning |
| url | https://ieeexplore.ieee.org/document/9096319/ |
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