Advances in Engineering Innovation
Vol. 4, 27 November 2023
Open
Access | Article
Joint detection algorithm for multiple cognitive users in spectrum sensing
* Author to whom correspondence should be addressed.
Abstract
Spectrum sensing technology is a crucial aspect of modern communication technology, serving as one of the essential techniques for efficiently utilizing scarce information resources in tight frequency bands. This paper first introduces three common logical circuit decision criteria in hard decisions and analyzes their decision rigor. Building upon hard decisions, the paper further introduces a method for multi-user spectrum sensing based on soft decisions. Then the paper simulates the false alarm probability and detection probability curves corresponding to the three criteria. The simulated results of multi-user collaborative sensing indicate that the simulation process significantly reduces false alarm probability and enhances detection probability. This approach effectively detects spectrum resources unoccupied during idle periods, leveraging the concept of time-division multiplexing and rationalizing the redistribution of information resources. The entire computation process relies on the calculation principles of power spectral density in communication theory, involving threshold decision detection for noise power and the sum of noise and signal power. It provides a secondary decision detection, reflecting the perceptual decision performance of logical detection methods with relative accuracy.
Keywords
multi-user collaboration perception; energy detection; dual energy threshold; false alarm probability; detection probability
References
1. Strite S and Morkoc H 1992 J. Vac. Sci. Technol. B 10 1237
2. Chen, K.; Liang, B.; Ke, W.; Xu, B.; Zeng, G.C. Chinese Micro—Blog Sentiment Analysis Based on Multi-Channels Convolutional Neural Networks. J. Comput. Res. Dev. 2018, 55, 945–957.
3. Richard Socher, Brody Huval, Christopher D. Manning, and Andrew Y. Ng. 2012. Semantic compositionality through recursive matrix-vector spaces. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1201–1211.
4. Vaswani A , Shazeer N , Parmar N , et al. Attention Is All You Need[J]. arXiv, 2017.
5. Radford, A., Narasimhan, K., Salimans, T. and Sutskever, I., 2018. Improving language understanding by generative pre-training.
6. Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. Bert: Pre-training of deep bidirectional transformers for language understanding.
7. Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y. and Potts, C., 2011, June. Learning word vectors for sentiment analysis. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies-volume 1 (pp. 142-150). Association for Computational Linguistics.
8. Branden Ghena Fanfei Meng. Research on text recognition methods based on artificial intelligence and machine learning. preprint under review, 2023.
9. Fanfei Meng and David Demeter. Sentiment analysis with adaptive multi-head attention in transformer, 2023.
10. Manijeh Razeghi, Arash Dehzangi, Donghai Wu, Ryan McClintock, Yiyun Zhang, Quentin Durlin, Jiakai Li, and Fanfei Meng. Antimonite-based gap-engineered type-ii superlattice materials grown by mbe and mocvd for the third generation of infrared imagers. In Infrared Technology and Applications XLV, volume 11002, pages 108–125. SPIE, 2019.
11. Fanfei Meng, Lele Zhang, and Yu Chen. Fedemb: An efficient vertical and hybrid federated learning algorithm using partial network embedding.
12. Fanfei Meng, Lele Zhang, and Yu Chen. Sample-based dynamic hierarchical trans-former with layer and head flexibility via contextual bandit.
13. Fanfei Meng and Chen-Ao Wang. Adynamic interactive learning interface for computer science education: Program-ming decomposition tool.
14. Chang Ling, Chonglei Zhang, Mingqun Wang, Fanfei Meng, Luping Du, and Xiaocong Yuan, "Fast structured illumination microscopy via deep learning," Photon. Res. 8, 1350-1359 (2020)
15. Meng, Fanfei, Lalita Jagadeesan, and Marina Thottan. "Model-based reinforcement learning for service mesh fault resiliency in a web application-level." arXiv preprint arXiv:2110.13621 (2021).
16. Chen, Jin-Jin, et al. "A dataset of diversity and distribution of rodents and shrews in China." Scientific Data 9.1 (2022): 304
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- ISBN (Print)
- 2977-3903
- ISBN (Online)
- 2977-3911
- Published Date
- 27 November 2023
- Series
- Advances in Engineering Innovation
- ISSN (Print)
- 2977-3903
- ISSN (Online)
- 2977-3911
- DOI
- 10.54254/2977-3903/4/2023053
- Copyright
- 27 November 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited