Journal of Scientific and Industrial Research

Open Access Open Access  Restricted Access Subscription Access

Machine Learning-Based Cooperative Spectrum Sensing in A Generalized α-κ-μ Fading Channel


Affiliations
1 School of Electronics Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha 751 024, India
 

An improvement in spectrum usage is possible with the help of a cognitive radio network, which allows secondary users’ access to the unused licensed frequency band of a primary user. Thus, spectrum sensing is a fundamental concept in cognitive radio networks. In recent years, Cooperative spectrum sensing using machine learning has garnered a great deal of attention as a technique of enhancing sensing capability. In this study, K-means clustering is taken into consideration for the purpose of analyzing the effectiveness of cooperative spectrum sensing in a generalized α-κ-μ fading channel. The proposed approach is examined using receiver operating characteristic curves to determine its performance. The effectiveness of the proposed strategy is contrasted with that of the existing detection techniques such as Cooperating spectrum sensing based on energy detection and OR-fusion-based cooperative spectrum sensing for fading channels κ-μ, α-κ-μ. As demonstrated by results, the proposed method outshines an existing method in terms of comparison parameters, as determined by simulation results in the MATLAB version.

Keywords

Cooperative Spectrum Sensing, Classification, κ-Means Clustering, α-κ-μ Channel.
User
Notifications
Font Size

  • Jun L, Zhang G &Yan C, An energy detection-based spectrum-sensing method for cognitive radio, Wirel Commun Mob Comput, 2022 (2022) 1–10, https://doi.org/10.1155/2022/3933336.

  • Rania A, Mokhtar, Rashid A, Saeed & Hesham A, Cooperative fusion architecture-based distributed spectrum sensing under rayleigh fading channel, Wirel Pers Commun, 124 (2022) 1–27, https://doi.org/10.1007/s11277-021-09386-z.

  • Eleutherius C, Ben A, Vladan V, Petros K & Justin C, Energy detection-based spectrum sensing over two-wave with diffuse power fading channels, IEEE Trans Veh Technol, 66 (2016) 868–874, doi:10.1109/TVT.2016.2556084.

  • Ma J, Zhao G & Li Y, Soft combination and detection for cooperative spectrum sensing in cognitive radio networks, IEEE Trans Wirel Commun, 7 (2008) 4502–4507, doi: 10.1109/T-WC.2008.070941.

  • Tavares C H A, Marinello J C, Proenca Jr & Abrao T, Machine learning-based models for spectrum sensing in cooperative radio networks, IET Commun, 14 (2020) 3102–3109, doi: 10.1049/iet-com.2019.0941.

  • Chembe C, Ahmedy I, Noor R M, Kunda D, Oche M & Tambawal A B, Cooperative spectrum decision in cognitive vehicular network based on support vector machine, Malays J Comput Sci, 32(2.1) (2019) 83–96, https://doi.org/10.22452/mjcs.vol32no2.1.

  • Sarikhani R & Keynia F, Cooperative spectrum sensing meets machine learning: deep reinforcement learning approach, IEEE Commun Lett, 24 (2020) 1459–1462, doi: 10.1109/LCOMM.2020.2984430.

  • Paschalis C, Sofotasios, Alireza B, Theodoros A T, Steven F, Ali S & Mikko V, A comprehensive framework for spectrum sensing in non-linear and generalized fading conditions, IEEE Trans Veh Technol, 66 (2017) 8615–8631, doi: 10.1109/TVT.2017.2692278.

  • Ramirez E P, Moualeu J M, Da C D B & Lopez M F J, The alpha-k-µ shadowed fading distribution: statistical characterization and applications, In IEEE Glob Commun Conf (Waikoloa, HI, USA) 2019, 1–6, doi: 10.1109/GLOBECOM38437.2019.9013399.

  • Al Hmood H & Al-Raweshidy H, On the effective rate and energy detection-basedspectrum sensing over α-η-k-μ fading channels, IEEE Trans Veh Technol, 69 (2020) 9112–9116, doi: 10.1109/TVT.2020.2998895.

  • Saman A, Chintha T & Hai J, Energy detection based cooperative spectrum sensing in cognitive radio networks, IEEE Trans Wirel Commun, 10 (2011) 1232–1241, doi: 10.1109/TWC.2011.012411.100611.

  • Ai Y, Kong L & Cheffena M, Secrecy outage analysis of double shadowed rician channels, Electron Lett, 55 (2019) 765–767,https://doi.org/10.1049/el.2019.0707.

  • Awe O P, Babatunde D A, Lambotharan S & Assadhan B, Second-order kalman filtering channel estimation and machine learning methods for spectrum sensing in cognitive radio networks, Wirel Netw, 27 (2021) 3273–3286, https://doi.org/10.1007/s11276-021-02627-w.

  • Huanlai X, Haoxiang Q, Shouxi L, Penglin D, Lexi X & Xinzhou C, Spectrum sensing in cognitive radio: a deep learning-based model, Trans Emerg Telecommun Technol, 33 (2022) 1–17, https://doi.org/10.1002/ett.4388.

  • Thilina K M, ChoiK W, Saquib N & Hossain E, Machine learning techniques for cooperative spectrum sensing in cognitive radio networks, IEEE J Sel Areas Commun, 31 (2013) 2209–2221, doi: 10.1109/JSAC.2013.131120.

  • Francisco R A P, Flavio D P C & Jose CSSF, Asymptotic system performance over generalized fading channels with application to maximal-ratio combining, J Commun Inf Syst, 35 (2020) 171–180, https://doi.org/10.14209/jcis.2020.18.

  • Singh B K & Mukhopadhyay M, Energy detector-based spectrum sensing performance analysis over fading environment, J Sci Ind Res, 80(3) (2021) 239–244, doi: 10.56042/jsir.v80i03.39666.

  • Debasish B, Indrajit C, Sant S P & George K K, Another look in the analysis of cooperative spectrum sensing over nakagami-m fading channels, IEEE Trans Wirel Commun, 16 (2016) 856–871, doi: 10.1109/TWC.2016.2633259.

  • Ehab S & Ali H, Performance analysis of α-η-μ and α-κ-μ generalized mobile fading channels, Eur Wirel Conf (VDE VERLAG GMBH, Berlin, Offenbach, Germany) 2014, 992–997.

  • Al-Rawi M, Performance measurement of one-bit hard decision fusion scheme for cooperative spectrum sensing in CR, Int Rev Appl Sci Eng, 8 (2017) 9–16, doi: 10.1556/1848.2017.8.1.3.

  • Hasan M M, Islam M M, Hussain M I & Rahman S M, Improvement of energy detection-basedspectrum sensing in cognitive radio network using adaptive threshold, IOSR J Electron Commun Eng, 13 (2018) 2278–2834, doi: 10.9790/2834-1302011120

  • Vaibhav K, Deep C K, Monika J, Ranjan G & Soumitra D, K-mean clustering-based cooperative spectrum sensing in generalized к-μ fading channels, In Twenty-Second Natl Conf Commun (NCC) (Guwahati, India) 2016, 1–5, doi: 10.1109/NCC.2016.7561130


Abstract Views: 54

PDF Views: 52




  • Machine Learning-Based Cooperative Spectrum Sensing in A Generalized α-κ-μ Fading Channel

Abstract Views: 54  |  PDF Views: 52

Authors

Srinivas Samala
School of Electronics Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha 751 024, India
Subhashree Mishra
School of Electronics Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha 751 024, India
Sudhansu Sekhar Singh
School of Electronics Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha 751 024, India

Abstract


An improvement in spectrum usage is possible with the help of a cognitive radio network, which allows secondary users’ access to the unused licensed frequency band of a primary user. Thus, spectrum sensing is a fundamental concept in cognitive radio networks. In recent years, Cooperative spectrum sensing using machine learning has garnered a great deal of attention as a technique of enhancing sensing capability. In this study, K-means clustering is taken into consideration for the purpose of analyzing the effectiveness of cooperative spectrum sensing in a generalized α-κ-μ fading channel. The proposed approach is examined using receiver operating characteristic curves to determine its performance. The effectiveness of the proposed strategy is contrasted with that of the existing detection techniques such as Cooperating spectrum sensing based on energy detection and OR-fusion-based cooperative spectrum sensing for fading channels κ-μ, α-κ-μ. As demonstrated by results, the proposed method outshines an existing method in terms of comparison parameters, as determined by simulation results in the MATLAB version.

Keywords


Cooperative Spectrum Sensing, Classification, κ-Means Clustering, α-κ-μ Channel.

References