Abstract
In an advance world, the field of information technology is full of contemporary wireless approaches as because of this the spectral efficiency and spectral sensing technology brings a broad interest in the Cooperative Cognitive Radio Network (CCRN). Besides, the Green metric CCRN spectral sharing is one of the efficient modern approaches compared with other traditional wireless technologies. Nevertheless, the distribution of spectral between the cooperative cognitive customers has dramatic challenges due to the collaboration between the licensed and unlicensed customers. Therefore, the motivation of this research work is to show the better performance of green metric CCRN using novel techniques. Here, a novel Markov Gaussian Wideband Distribution (MGWD) and Dynamic First-Rate Relay Protocol is developed for communication. Moreover, a novel Intellectual African Buffalo Optimization (IABO) is proposed to estimate the finest spectral sensing and false alarm detection. The implementation of this work is done on the MATLAB platform. Sequentially, the performance of green metric CCRN is estimated in terms of the communication ratio, spectral efficiency, outage probability, and throughput. Moreover, the simulation outcomes show that the developed method in CCRN attain higher throughput and diminished power consumption in terms of both Primary User (PU) and Secondary User (SU) successful transmission of probability compared with the other conventional strategies.
Similar content being viewed by others
References
Akyildiz IF, Lee WY, Vuran MC, Mohanty S (2006) NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput Netw 50(13):2127–2159. https://doi.org/10.1016/j.comnet.2006.05.001
Ali A, Abbas L, Shafiq M, Bashir AK, Afzal MK, Liaqat HB, Kwak KS (2019) Hybrid fuzzy logic scheme for efficient channel utilization in cognitive radio networks. IEEE Access 7:24463–24476. https://doi.org/10.1109/ACCESS.2019.2900233
Badoi CI, Prasad N, Croitoru V, Prasad R (2011) 5G based on cognitive radio. Wirel Pers Commun 57(3):441–464. https://doi.org/10.1007/s11277-010-0082-9
Banerjee A, Maity SP, Das RK (2018) On throughput maximization in cooperative cognitive radio networks with eavesdropping. IEEE Commun Lett 23(1):120–123. https://doi.org/10.1109/LCOMM.2018.2875749
Bouallegue K, Dayoub I, Gharbi M, Hassan K (2017) Blind spectrum sensing using extreme eigenvalues for cognitive radio networks. IEEE Commun Lett 22(7):1386–1389. https://doi.org/10.1109/LCOMM.2017.2776147
Boukredera D, Adel-Aissanou K (2020) Modeling and performance analysis of cognitive radio networks using stochastic timed colored petri nets. Wirel Pers Commun. https://doi.org/10.1007/s11277-020-07121-8
Carie A, Li M, Marapelli B, Reddy P, Dino H (2019) Cognitive radio assisted WSN with interference aware AODV routing protocol. J Amb Intel Hum Comp 10(10):4033–4042. https://doi.org/10.1007/s12652-019-01282-6
Chatterjee S, Maity SP, Acharya T (2016) Energy efficiency in cooperative cognitive radio network in the presence of malicious users. IEEE Syst J 12(3):2197–2206. https://doi.org/10.1109/JSYST.2016.2631219
Ejaz W, ul Hasan N, Shah GA, Kim HS, Anpalagan A (2016) Biologically inspired cooperative spectrum sensing scheme for maritime cognitive radio networks. IEEE Syst J 12(3):2141–2151. https://doi.org/10.1109/JSYST.2016.2578260
Elangovan K, Subashini S (2018) Particle bee optimized convolution neural network for managing security using cross-layer design in cognitive radio network. J Amb Intel Hum Comp. https://doi.org/10.1007/s12652-018-1007-9
Gür G, Alagöz F (2011) Green wireless communications via cognitive dimension: an overview. IEEE Netw 25(2):50–56. https://doi.org/10.1109/MNET.2011.5730528
Jaglan RR, Mustafa R, Agrawal S (2018) Scalable and robust ANN based cooperative spectrum sensing for cognitive radio networks. Wirel Pers Commun 99(3):1141–1157. https://doi.org/10.1007/s11277-017-5168-1
Kang X, Liang YC, Nallanathan A (2009) Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity. IEEE Trans Wirel Commun 8(2):940–950. https://doi.org/10.1109/TWC.2009.071448
Karmokar A, Naeem M, Anpalagan A (2018) Green metric optimization in cooperative cognitive radio networks with statistical interference parameters. IEEE Syst J 12(1):1034–1037. https://doi.org/10.1109/JSYST.2015.2451996
Kumar A, Thakur P, Pandit S (2020) Intelligent threshold selection in fading environment of cognitive radio network: Advances in throughput and total error probability. Int J Commun Syst 33(1):e4175. https://doi.org/10.1002/dac.4175
Liu Y, Cai LX, Shen XS (2012) Spectrum-aware opportunistic routing in multi-hop cognitive radio networks. IEEE J Sel Areas Commun 30(10):1958–1968. https://doi.org/10.1109/JSAC.2012.121111
Nasser A, Mansour A, Yao KC (2020) Simultaneous transmitting–receiving–sensing for OFDM-based full-duplex cognitive radio. Phys Commun 39:100987. https://doi.org/10.1016/j.phycom.2019.100987
Pramanik PKD, Pal S, Choudhury P (2018) Beyond automation: the cognitive IoT artificial intelligence brings sense to the Internet of Things. Cognitive Computing for Big Data Systems Over IoT, Springer, Cham, pp 1–37. https://doi.org/10.1007/978-3-319-70688-7_1
Rahim M, Alfakeeh AS, Hussain R, Javed MA (2020) Efficient channel allocation using matching theory for QoS provisioning in cognitive radio networks. Sensors 20(7):1872. https://doi.org/10.3390/s20071872
Shen JC (2013) Detection and Estimation Techniques in Cognitive Radio. Diss. The University of Manchester (United Kingdom)
Singh A, Bhatnagar MR, Mallik RK (2011) Cooperative spectrum sensing in multiple antenna based cognitive radio network using an improved energy detector. IEEE Commun Lett 16(1):64–67. https://doi.org/10.1109/LCOMM.2011.103111.111884
Yadav R, Kumar A, Singh K (2019) Green power allocation for cognitive radio networks with spectrum sensing. IEEJ Trans Electr Electron Eng 14(3):403–410. https://doi.org/10.1002/tee.22821
Yadav K, Roy SD, Kundu S (2020) Defense against spectrum sensing data falsification attacker in cognitive radio networks. Wirel Pers Commun. https://doi.org/10.1007/s11277-020-07077-9
Yang J, Xu H, Zhang J (2018a) Exploiting secondary caching for cooperative cognitive radio networks. IEEE Commun Lett 23(1):124–127. https://doi.org/10.1109/LCOMM.2018.2877383
Yang Z, Ping S, Aijaz A, Aghvami AH (2018b) A global optimization-based routing protocol for cognitive-radio-enabled smart grid AMI networks. IEEE Syst J 12(1):1015–1023. https://doi.org/10.1109/JSYST.2016.2580616
Yin M, Li K, Zheng M (2018) Spectrum utilization of cognitive radio in industrial wireless sensor networks—a review. Intelligent computing and internet of things, Springer, Singapore, pp 419–428. https://doi.org/10.1007/978-981-13-2384-3_39
Yu C, Lee B, Yong Youn H (2003) Energy efficient routing protocols for mobile ad hoc networks. Wirel Commun Mob Comput 3(8):959–973. https://doi.org/10.1002/wcm.119
Zeng F, Xu J, Li Y, Li K, Jiao L (2018) Performance analysis of underlay two-way relay cooperation in cognitive radio networks with energy harvesting. Comput Netw 142:13–23. https://doi.org/10.1016/j.comnet.2018.05.023
Zhang L, Zhuo F, Xu H (2018) A cross-layer optimization framework for congestion and power control in cognitive radio ad hoc networks under predictable contact. EURASIP J Wirel Commun Netw 1:57. https://doi.org/10.1186/s13638-018-1065-x
Zhao N, Yu FR (2013) Sun H (2013) Energy-efficient cooperative spectrum sensing schemes for cognitive radio networks. EURASIP J Wirel Commun Netw 1:1–13. https://doi.org/10.1186/1687-1499-2013-120
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no potential conflict of interest.
Ethical approval
All applicable institutional and/or national guidelines for the care and use of animals were followed.
Informed consent
For this type of study formal consent is not required.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kumar, M.A., Siddaiah, P. Spectral efficiency enhancement of green metric cognitive radio network using novel channel design and intellectual African buffalo optimization. J Ambient Intell Human Comput 13, 3229–3243 (2022). https://doi.org/10.1007/s12652-021-03159-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-021-03159-z