Abstract
Modern radio networks promote the usage and emergence of new age technologies through enabling lay users to utilize superior gadgets without external assistance. Cognitive Radio technology, an emergent new age product, established the possibility for unlicensed cognitive users to access radio frequencies across a spectrum hole and understand its implications via spectrum sensing mechanisms. Since unlicensed users are not one of the primary groups that utilize the above technology, it poses a challenge to the use of spectrum prediction as there are several subtopics under this category, namely, prediction of channel statuses, ‘activities of Primary Users’, environment of radio and rate of transmission. In this paper, a new class of optimization heuristics called hybrid optimization is used. This will implement two or more algorithms for the same optimization. A Genetic Algorithm along with Particle Swarm Optimization (GAPSO) method is proposed with a Back-Propagation Neural Network (BPNN) as a novel supervised learning algorithm for predicting spectrum patterns in cognitive radio networks.
Similar content being viewed by others
References
Kim S-J, Dall’Anese E, Bazerque JA, Rajawat K, Giannakis GB (2013) Advances in spectrum sensing and cross- layer design for cognitive radio networks. In: E-Reference Signal Processing. EURASIP
Kim SJ, Giannakis G (2013) Cognitive radio spectrum prediction using dictionary learning. In: global communications conference (GLOBECOM), 2013 IEEE. IEEE, pp 3206-3211
Xing X, Jing T, Cheng W, Huo Y, Cheng X (2013) Spectrum prediction in cognitive radio networks. Wireless Comm, IEEE 20(2):90–96
El-Hajj W, Safa H, Guizani M (2011) Survey of security issues in cognitive radio networks. J Inter Technol 12(2):181–198
Yildiz AR (2013) Comparison of evolutionary-based optimization algorithms for structural design optimization. Eng Appl Artif Intell 26(1):327–333
Venkatesan M, Kulkarni AV, Menon R (2013) Artificial neural network based learning in cognitive radio. International Journal of Computer, Electrical, Automation, Control and Information Engineering 9:1
Lan K, Zhao H, Zhang J, Long C, Luo M (2014) A spectrum prediction approach based on neural networks optimized by genetic algorithm in cognitive radio networks. In: wireless communications, networking and mobile computing (WiCOM 2014), 10th international conference on. IET, pp 131-136
Tumuluru VK, Wang P, Niyato D (2012) Channel status prediction for cognitive radio networks. Wirel Commun Mob Comput 12(10):862–874
Tang M, Long C, Guan X, Wei X (2012) Nonconvex dynamic spectrum allocation for cognitive radio networks via particle swarm optimization and simulated annealing. Comput Netw 56(11):2690–2699
Karaboga D, Kaya E (2016) An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training. Appl Soft Comput 49:423–436
Karaboga D, Aslan S (2016) Best supported emigrant creation for parallel implementation of artificial bee Colony algorithm. IU-JElectric Electron Eng 16(2):2055–2064
Ansari IA, Pant M, Ahn CW (2016) Artificial bee colony optimized robust-reversible image watermarking. Journal of Multimedia Tools and Applications 76(17):18001–18025
Wang H, Wang J (2014) An effective image representation method using kernel classification. Tools with Artificial Intelligence (ICTAI), 2014 I.E. 26th international conference on. IEEE
Wang N, Yeung D-Y (2013) Learning a deep compact image representation for visual tracking. Advances in neural information processing systems. Adv Neural Inf Proces Syst 26:809–817
Zhang S, Wang H, Huang W (2017) Two-stage plant species recognition by local mean clustering and weighted sparse representation classification. Clust Comput 20(2):1–9
Zhang H et al (2014) A nonlocal weighted joint sparse representation classification method for hyperspectral imagery. IEEE J Sel Topics Appl Earth Observ Remote Sens 7(6):2056–2065
Chan W et al (2016) Listen, attend and spell: a neural network for large vocabulary conversational speech recognition. Acoustics, speech and signal processing (ICASSP), 2016 I.E. international Conference on IEEE
Wu Z, Wang H (2016) Super-resolution reconstruction of SAR image based on non-local means denoising combined with BP neural network. Article in computer vision and pattern recognition Cornell University library. arXiv preprint arXiv:1612.04755
Shoaib M et al (2016) Hybrid wavelet neural network approach. Artificial neural network modelling. Part of the Studies in Computational Intelligence book series. Springer International Publishing 628:127–143
Rehman MZ, Nazri MN (2012) Studying the effect of adaptive momentum in improving the accuracy of gradient descent back propagation algorithm on classification problems. Inter- nat J Modern Phys (IJMPCS) 9(1):432–439
Ozturk C, Karaboga D (2011) Hybrid artificial bee colony algorithm for neural network training. In: IEEE congress of evolutionary computation (CEC), pp 84–88
Wang T, Gao H, Qiu J (2016) A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Trans Neural Net Learn Syst 27(2):416–425
Nawi NM, Khan A, Rehman MZ (2013) A new back-propagation neural network optimized with cuckoo search algorithm. In: computational science and its applications–ICCSA 2013 (pp. 413-426). Springer, Berlin
Eberhart RC, Shi Y Comparison between genetic algorithms and particle swarm optimization. In: Proc. of the 7th international Conference on Evolutionary Programming VII, vol 1447. Springer-Verlag, London, pp 611–616
Kennedy J, Eberhart R (1995) Partical swarm optimization. In: Proc. IEEE International Conference on Neural Networks, Perth, pp 1942-1948
Cheng L, Liu J (2013) Automatic modulation classifier using artificial neural network trained by PSO algorithm. Aust J Commun 8(5)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Supraja, P., Pitchai, R. & Raja Spectrum Prediction in Cognitive Radio with Hybrid Optimized Neural Network. Mobile Netw Appl 24, 357–364 (2019). https://doi.org/10.1007/s11036-017-0909-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-017-0909-7