RNN-BIRNN-LSTM based spectrum sensing for proficient data transmission in cognitive radio
With the advancements that are taking place in the wireless communications field, the number of users who are utilizing resources is also increasing; as a result, the wireless spectrum is scarce. In this article, RNN-BIRNN-LSTM with Gaussian noise (RBRLG)-based spectrum sensing (SS) for QAM16, CPFSK...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671123002735 |
| _version_ | 1797388616428683264 |
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| author | E．Vargil Vijay K. Aparna |
| author_facet | E．Vargil Vijay K. Aparna |
| author_sort | E．Vargil Vijay |
| collection | DOAJ |
| description | With the advancements that are taking place in the wireless communications field, the number of users who are utilizing resources is also increasing; as a result, the wireless spectrum is scarce. In this article, RNN-BIRNN-LSTM with Gaussian noise (RBRLG)-based spectrum sensing (SS) for QAM16, CPFSK, QPSK, and BPSK modulation schemes has been proposed. Recurrent Neural Networks for sequential data use recurrent connections to capture temporal dependencies; BIRNN extends RNN by processing input in both forward and backward directions, capturing past and future context; and finally, LSTM, using specialized memory cells, efficiently manages long-term dependencies in sequential data. In order to create a spectrum sensing model, RNN units, BIRNN units, and LSTM units were cascaded in this paper. Open-source dataset RadioML2016.10B has been used for the investigation. The experimental results show that the proposed RBRLG-based SS has higher accuracy on the dataset especially at -20 dB, a lower probability of miss detection percentage of 7.19 %, and a lower sensing error (SE) percentage of 10.80 % for QAM16. The evaluation of performance indicators for our suggested model, such as the F1 Score, Jaccard Index, and Matthew's correlation coefficient, demonstrates that the proposed model provides improved SS performance. |
| first_indexed | 2024-03-08T22:43:21Z |
| format | Article |
| id | doaj.art-7ce4c7f69d304dada16606e1b12a87cd |
| institution | Directory Open Access Journal |
| issn | 2772-6711 |
| language | English |
| last_indexed | 2024-03-08T22:43:21Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj.art-7ce4c7f69d304dada16606e1b12a87cd2023-12-17T06:43:36ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112023-12-016100378RNN-BIRNN-LSTM based spectrum sensing for proficient data transmission in cognitive radioE．Vargil Vijay0K. Aparna1Electronics & Communication Engineering Department, Jawaharlal Nehru Technological University Anantapur (JNTUA), Ananthapuramu, Andhra Pradesh, India; Corresponding author.ECE Department, JNTUA College of Engineering Kalikiri (JNTUACEK), Kalikiri, Constituent College of Jawaharlal Nehru Technologicial University Anantapur, Ananthapuramu, Andhra Pradesh, IndiaWith the advancements that are taking place in the wireless communications field, the number of users who are utilizing resources is also increasing; as a result, the wireless spectrum is scarce. In this article, RNN-BIRNN-LSTM with Gaussian noise (RBRLG)-based spectrum sensing (SS) for QAM16, CPFSK, QPSK, and BPSK modulation schemes has been proposed. Recurrent Neural Networks for sequential data use recurrent connections to capture temporal dependencies; BIRNN extends RNN by processing input in both forward and backward directions, capturing past and future context; and finally, LSTM, using specialized memory cells, efficiently manages long-term dependencies in sequential data. In order to create a spectrum sensing model, RNN units, BIRNN units, and LSTM units were cascaded in this paper. Open-source dataset RadioML2016.10B has been used for the investigation. The experimental results show that the proposed RBRLG-based SS has higher accuracy on the dataset especially at -20 dB, a lower probability of miss detection percentage of 7.19 %, and a lower sensing error (SE) percentage of 10.80 % for QAM16. The evaluation of performance indicators for our suggested model, such as the F1 Score, Jaccard Index, and Matthew's correlation coefficient, demonstrates that the proposed model provides improved SS performance.http://www.sciencedirect.com/science/article/pii/S2772671123002735Spectrum sensingWireless spectrumProbability of detectionSensing error |
| spellingShingle | E．Vargil Vijay K. Aparna RNN-BIRNN-LSTM based spectrum sensing for proficient data transmission in cognitive radio e-Prime: Advances in Electrical Engineering, Electronics and Energy Spectrum sensing Wireless spectrum Probability of detection Sensing error |
| title | RNN-BIRNN-LSTM based spectrum sensing for proficient data transmission in cognitive radio |
| title_full | RNN-BIRNN-LSTM based spectrum sensing for proficient data transmission in cognitive radio |
| title_fullStr | RNN-BIRNN-LSTM based spectrum sensing for proficient data transmission in cognitive radio |
| title_full_unstemmed | RNN-BIRNN-LSTM based spectrum sensing for proficient data transmission in cognitive radio |
| title_short | RNN-BIRNN-LSTM based spectrum sensing for proficient data transmission in cognitive radio |
| title_sort | rnn birnn lstm based spectrum sensing for proficient data transmission in cognitive radio |
| topic | Spectrum sensing Wireless spectrum Probability of detection Sensing error |
| url | http://www.sciencedirect.com/science/article/pii/S2772671123002735 |
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