Abstract
Kablosuz iletişim için giderek artan gereksinim ve spektrumun sınırlı doğası göz önüne alındığında, bilişsel radyo teknolojisi radyo frekansı spektrumunun kullanımının optimize edilmesinde çok önemli bir rol oynamaktadır. Spektrum algılama, bilişsel radyo ağının temel işlevidir. Bu makalede, yakın zamanda geliştirilen Sürüngen Arama Algoritması (RSA), bilişsel radyo sistemleri için işbirlikçi spektrum algılamada tespit yeteneklerini artırmak amacıyla kullanılmıştır. Yumuşak füzyon şeması yardımıyla ikincil kullanıcılara ağırlık atamaları gerçekleştirildi ve bu atamaların en yüksek tespit sonuçlarını vermesini sağlamak için Sürüngen Arama Algoritmasını kullanıldı. Sonuçlar diğer iki optimizasyon algoritması olan Parçacık Sürü Optimizasyonu (PSO) ve Aquila Optimizer (AO) ile karşılaştırılarak Sürüngen Arama Algoritmasının diğer algoritmalara göre daha iyi sonuçlar sağladığı görülmüştür.
Keywords
Bilişsel Radyo Sürüngen Arama Algoritması Ağırlık Katsayıları Optimizasyonu Algılama Performansı
Project Number
FYL-2022-12479
References
- S. Haykin “Cognitive radio: Brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201–220, 2005.
- B. Wang, K.J.R. Liu “Advances in Cognitive radio networks: A survey,” IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 1, pp. 5–23, 2011.
- P.Verma, B.Singh “On the decision fusion for cooperative spectrum sensing in cognitive radio networks,” Wireless Networks, vol. 23, no. 7, pp. 2253–2262, 2016.
- I.F. Akyildiz, W.-Y. Lee, M.C. Vuran, and S. Mohanty “NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey,” Computer Networks, vol. 50, no. 13, pp. 2127–2159, 2006.
- W. Zhang, R. Mallik, and K. Letaief “Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 8, no. 12, pp. 5761–5766, 2009.
- R. Vadivelu, K. Sankaranarayanan, and V. Vijayakumari “Matched filter based spectrum sensing for cognitive radio at low signal to noise ratio,” Journal of Theoretical and Applied Information Technology , vol. 62, no. 1, 2014.
- K. Kim, I.A. Akbar, K.K. Bae, J.-S. Um, C.M. Spooner, and J.H. Reed “Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio,” 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 212-215, 2007.
- Y.-C. Liang, K.-C. Chen, G. Y. Li, and P. Mahonen, “Cognitive radio networking and communications: an overview,” IEEE Transactions on Vehicular Technology, vol. 60, no. 7, pp. 3386–3407, Sep. 2011.
- Z. Quan, S. Cui, H. Vincent Poor, and A.H. Sayed “Collaborative wideband sensing for cognitive radios,” IEEE Signal Processing Magazine, vol. 25, no. 6, pp. 60–73, 2008.
- Z. Quan, S. Cui, and A.H. Sayed “Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 28–40, 2008.
- J. Ma, G. Zhao, and Y. Li “Soft combination and detection for cooperative spectrum sensing in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 7, no. 11, pp. 4502–4507, 2008.
- H. Sakran, M. Shokair “Hard and softened combination for cooperative spectrum sensing over imperfect channels in cognitive radio networks,” Telecommunication Systems, vol. 52, no. 1, pp. 61–71, 2011.
- D. Teguig, B. Scheers, and Vincent Le Nir “Data fusion schemes for cooperative spectrum sensing in cognitive radio networks,” 2012 Military Communications and Information Systems Conference (MCC), pp. 1–7, Gdansk,Poland, 2012.
- T.M. Shami, A.A. El-Saleh, and A.M. Kareem “On the detection performance of cooperative spectrum sensing using particle swarm optimization algorithms,” IEEE 2nd International Symposium on Telecommunication Technologies, pp. 110–114, Langkawi, Malaysia, 2014.
- A.A. El-Saleh, M. Ismail, and M.A.M. Ali “Genetic algorithm-assisted soft fusion-based linear cooperative spectrum sensing,” IEICE Electronics Express, vol. 8, no. 18, pp. 1527–1533, 2011.
- M. Akbari, M. Ghanbarisabagh “A novel evolutionary-based cooperative spectrum sensing mechanism for cognitive radio networks”, Wireless Personal Communications, vol. 79, no. 2, pp. 1017-1030, 2014.
- X. Li, L. Lu, L. Liu, G.Li, X.Guan “Cooperative spectrum sensing based on an efficient adaptive artificial bee colony algorithm”, Soft Computing, vol. 19, no. 13, pp. 597-607, 2015.
- F. Azmat, Y. Chen and N. Stocks “Bio-inspired collaborative spectrum sensing and allocation for cognitive radios”, IET Communications, vol. 9, no. 16, pp.1949-1959, 2015.
- L. Abualigah, M. A. Elaziz, P. Sumari, Z. W. Geem, and A. H. Gandomi “Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer,” Expert Systems with Applications, vol. 191, p. 116158, 2022.
- I. Al-Shourbaji, N. Helian, Y. Sun, S. Alshathri, and M. Abd Elaziz “Boosting Ant Colony Optimization with Reptile Search Algorithm for Churn Prediction,” Mathematics, vol. 10, no. 7, p. 1031, 2022.
Abstract
Given the growing requirement for wireless communication and the limited nature of the spectrum, cognitive radio technology plays a crucial role in optimizing the use of the radio frequency spectrum. Spectrum sensing is the core function of the cognitive radio network. In this paper, the recently developed Reptile Search Algorithm (RSA) is used to increase detection capabilities in cooperative spectrum sensing for cognitive radio systems. Weight assignments were made to secondary users with the help of soft fusion scheme and Reptile Search Algorithm was used to ensure that these assignments gave the highest detection results. The results were compared with the other two optimization algorithms, Particle Swarm Optimization (PSO) and Aquila Optimizer (AO), and it was seen that Reptile Search Algorithm provides better results than the other algorithms.
Keywords
Cognitive Radio Reptile Search Algorithm Weight Coefficients Optimization Detection Performance
Supporting Institution
Erciyes University Scientific Research Projects Coordination Unit
Project Number
FYL-2022-12479
Thanks
This work was supported by Erciyes University Scientific Research Projects Coordination Unit (Project No: FYL-2022-12479).
References
- S. Haykin “Cognitive radio: Brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201–220, 2005.
- B. Wang, K.J.R. Liu “Advances in Cognitive radio networks: A survey,” IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 1, pp. 5–23, 2011.
- P.Verma, B.Singh “On the decision fusion for cooperative spectrum sensing in cognitive radio networks,” Wireless Networks, vol. 23, no. 7, pp. 2253–2262, 2016.
- I.F. Akyildiz, W.-Y. Lee, M.C. Vuran, and S. Mohanty “NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey,” Computer Networks, vol. 50, no. 13, pp. 2127–2159, 2006.
- W. Zhang, R. Mallik, and K. Letaief “Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 8, no. 12, pp. 5761–5766, 2009.
- R. Vadivelu, K. Sankaranarayanan, and V. Vijayakumari “Matched filter based spectrum sensing for cognitive radio at low signal to noise ratio,” Journal of Theoretical and Applied Information Technology , vol. 62, no. 1, 2014.
- K. Kim, I.A. Akbar, K.K. Bae, J.-S. Um, C.M. Spooner, and J.H. Reed “Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio,” 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 212-215, 2007.
- Y.-C. Liang, K.-C. Chen, G. Y. Li, and P. Mahonen, “Cognitive radio networking and communications: an overview,” IEEE Transactions on Vehicular Technology, vol. 60, no. 7, pp. 3386–3407, Sep. 2011.
- Z. Quan, S. Cui, H. Vincent Poor, and A.H. Sayed “Collaborative wideband sensing for cognitive radios,” IEEE Signal Processing Magazine, vol. 25, no. 6, pp. 60–73, 2008.
- Z. Quan, S. Cui, and A.H. Sayed “Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 28–40, 2008.
- J. Ma, G. Zhao, and Y. Li “Soft combination and detection for cooperative spectrum sensing in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 7, no. 11, pp. 4502–4507, 2008.
- H. Sakran, M. Shokair “Hard and softened combination for cooperative spectrum sensing over imperfect channels in cognitive radio networks,” Telecommunication Systems, vol. 52, no. 1, pp. 61–71, 2011.
- D. Teguig, B. Scheers, and Vincent Le Nir “Data fusion schemes for cooperative spectrum sensing in cognitive radio networks,” 2012 Military Communications and Information Systems Conference (MCC), pp. 1–7, Gdansk,Poland, 2012.
- T.M. Shami, A.A. El-Saleh, and A.M. Kareem “On the detection performance of cooperative spectrum sensing using particle swarm optimization algorithms,” IEEE 2nd International Symposium on Telecommunication Technologies, pp. 110–114, Langkawi, Malaysia, 2014.
- A.A. El-Saleh, M. Ismail, and M.A.M. Ali “Genetic algorithm-assisted soft fusion-based linear cooperative spectrum sensing,” IEICE Electronics Express, vol. 8, no. 18, pp. 1527–1533, 2011.
- M. Akbari, M. Ghanbarisabagh “A novel evolutionary-based cooperative spectrum sensing mechanism for cognitive radio networks”, Wireless Personal Communications, vol. 79, no. 2, pp. 1017-1030, 2014.
- X. Li, L. Lu, L. Liu, G.Li, X.Guan “Cooperative spectrum sensing based on an efficient adaptive artificial bee colony algorithm”, Soft Computing, vol. 19, no. 13, pp. 597-607, 2015.
- F. Azmat, Y. Chen and N. Stocks “Bio-inspired collaborative spectrum sensing and allocation for cognitive radios”, IET Communications, vol. 9, no. 16, pp.1949-1959, 2015.
- L. Abualigah, M. A. Elaziz, P. Sumari, Z. W. Geem, and A. H. Gandomi “Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer,” Expert Systems with Applications, vol. 191, p. 116158, 2022.
- I. Al-Shourbaji, N. Helian, Y. Sun, S. Alshathri, and M. Abd Elaziz “Boosting Ant Colony Optimization with Reptile Search Algorithm for Churn Prediction,” Mathematics, vol. 10, no. 7, p. 1031, 2022.
Details
| Primary Language | English |
|---|---|
| Subjects | Wireless Communication Systems and Technologies (Incl. Microwave and Millimetrewave) |
| Journal Section | Research Articles |
| Authors | |
| Project Number | FYL-2022-12479 |
| Early Pub Date | October 18, 2023 |
| Publication Date | October 27, 2023 |
| Published in Issue | Year 2023 Volume: 5 Issue: 2 |
