Abstract
This paper in this topic concentrates on an important part is spectrum sensing (SS). It can detect the idle hole in spectrum by detection methods. This paper uses the sensing technique is called energy detector(ED). The ED depends on only the energy of the signal without other needs such as the modulation of signal or pre-knowledge about the signal and this is considered as advantage. This research proposed new two techniques are the additive wavelet transform (AWT) with Homomorphic Way (HW) and Haar Discrete Wavelet Transform (HDWT) approach. We apply these techniques are applied in wide band wireless signal by using the Cognitive Radio (CR) network. Each technique reduces the noise of signal before enter to the detection method ED. The HW is considered new technique in the wireless communication. This study will have these techniques as hybrid with the ED to increase the throughput for the cognitive user with a sufficient protection to the PU transmission. Also, it improves the probability of detection and reduces the probability of false alarm and the probability of error. The cooperative CR is used in this work which more than the non-cooperative cognitive user to detect the holes. The final decision for detection built on four fusion rules are the logic OR, logic AND, MAJORITY and K-Out-Of-M fusion rule. The two proposed are applied techniques on four fusion rule at constant sensing time. Then; study the four metric detection performances for each fusion rule by using the Additive White Gaussian Noise (AWGN) channel. At the end, comparison between two these proposed techniques with each fusion rule. Simulation results prove that the proposed scenario increases the probability of detection in the range of SNR of the PU from −20 to −5 dB using the theses proposed approaches.
Similar content being viewed by others
References
Abdulsattar MA, Hussein ZA (2012) Energy detection technique for spectrum sensing in cognitive radio: a survey. Int J Comput Netw Commun Secur 4(5):223
Abou ElHassan M et al (2019) Adaptively controlled cooperative Spectrum sensing using OR fusion rule for throughput maximization in cognitive radio. Wirel Pers Commun 109(4):2095–2105
Akyildiz IF et al (2006) NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput Netw 50(13):2127–2159
An C, Si P, Ji H (2011) "Wideband spectrum sensing scheme in cognitive radio networks with multiple primary networks." 2011 IEEE Wireless Communications and Networking Conference. IEEE
Ashiba HI (2020) Cepstrum adaptive plateau histogram for dark IR night vision images enhancement. Multimed Tools Appl 79:2543–2554
Ashiba HI, Awadallah KH, El-Halfawy SM, Abd El-Samie FE (2008) Homomorphic enhancement of infrared images using the additive wavelet transform. Prog Electromagn Res C 1:123–130
Ashiba HI, Mansour HM, Ahmed HM, El-Kordy MF, Dessouky MI, Zahran O, El-Samie FEA (2019) Enhancement of IR images using histogram processing and the Undecimated additive wavelet transform. Multimed Tools Appl 78(9):11277–11290
Barry JR, Lee EA, Messerschmitt DG (2012) Digital communication. Springer Science & Business Media
Chen X, Chen H-H, Meng W (2014) Cooperative communications for cognitive radio networks—from theory to applications. IEEE Commun Surv Tutor 16(3):1180–1192
Elhassan MA et al (2019) Throughput maximization for multimedia communication with cooperative cognitive radio using adaptively controlled sensing time. Multimed Tools Appl 78:4999–35025
Fan R, Jiang H (2010) Optimal multi-channel cooperative sensing in cognitive radio networks. IEEE Trans Wirel Commun 9(3):1128–1138
Ghasemi A, Sousa ES (2008) Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs. IEEE Commun Mag 46(4):32–39
Gomaa N et al (2021) Hybrid detection for cooperative cognitive radio using AWT and HDWT. Wirel Pers Commun 118(4):2151–2174
Haykin S (2005) Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun 23(2):201–220
Jakoubek RR, Zuber EO, Patel DP (2011) Software radio system and method. US Patent 7:885–409
Kaporis A et al (2020) Dynamic interpolation search revisited. Inf Comput 270:104–465
Kobeissi H, Bazzi O, Nasser Y (2013) "Wavelet denoising in cooperative and NonCooperative spectrum sensing." ICT 2013. IEEE
Liu X, Jia M, Tan X (2013) Threshold optimization of cooperative spectrum sensing in cognitive radio networks. Radio Sci 48(1):23–32
Liu Y, et al. (2015) "Action2Activity: recognizing complex activities from sensor data." Twenty-fourth international joint conference on artificial intelligence
Liu Y et al (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115
Lv Q, Gao F (2015) "Matched filter based spectrum sensing and power level recognition with multiple antennas," IEEE China Summit and International Conference on Signal and Information Processing, IEEE
Maleki S, Chepuri SP, Leus G (2011) "Energy and throughput efficient strategies for cooperative spectrum sensing in cognitive radios." 2011 IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications. IEEE
Parvathi S, Hemamalini S (2017) Dyadic wavelet transform-based acoustic signal analysis for torque prediction of a three-phase induction motor. IET Signal Process 11(5):604–612
Patil VM, Patil SR (2016) "A survey on spectrum sensing algorithms for cognitive radio." 2016 International Conference on Advances in Human Machine Interaction (HMI), IEEE
Peh E, Liang Y-C (2007) "Optimization for cooperative sensing in cognitive radio networks." 2007 IEEE Wireless Communications and Networking Conference. IEEE
Petrovic VS, Xydeas CS (2004) Gradient-based multiresolution image fusion. IEEE Trans Image Process 13(2):228–237
Plata DMM, Reátiga ÁGA (2012) Evaluation of energy detection for spectrum sensing based on the dynamic selection of detection-threshold. Procedia Eng 35:135–143
Rao AM et al (2010) Energy detection technique for spectrum sensing in cognitive radio. SASTECH 9(1):73–78
Su Y et al (2019) Elimination of systematic error in digital image correlation caused by intensity interpolation by introducing position randomness to subset points. Opt Lasers Eng 114:60–75
Tang L et al (2011) Effect of primary user traffic on sensing-throughput tradeoff for cognitive radios. IEEE Trans Wirel Commun 10(4):1063–1068
Tian Y et al (2018) Improved three-dimensional reconstruction algorithm from a multifocus microscopic image sequence based on a nonsubsampled wavelet transform. Appl Opt 57(14):3864–3872
Vadivelu R, Sankaranarayanan K, Vijayakumari V (2014) Matched filter based spectrum sensing for cognitive radio at low signal to noise ratio. J Theor Appl Inf Technol 62:1
Wang H, et al. (2010) "Cooperative spectrum sensing with wavelet denoising in cognitive radio." 2010 IEEE 71st Vehicular Technology Conference. IEEE
Xu JY, Alam F (2009) "Adaptive energy detection for cognitive radio: An experimental study." 2009 12th International Conference on Computers and Information Technology. IEEE
Yucek T, Arslan H (2009) A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutor 11(1):116–130
Zhang Q et al (2010) Introduction to the issue on cooperative communication and signal processing in cognitive radio systems. IEEE J Sel Top Signal Process 5(1):1–4
Author information
Authors and Affiliations
Corresponding author
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
Gomaa, N., Ashiba, H.I., El-Dolil, S.A. et al. Proposed Approaches for Cooperative Cognitive Radio. Multimed Tools Appl 81, 5645–5668 (2022). https://doi.org/10.1007/s11042-021-11703-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-021-11703-4