Abstract
In cooperative mobile communications, spectrum sensing performance may encounter difficulties, which alert with many reporting errors, especially in dense network scenarios. In such networks, the decision fusion process for cooperating users becomes very complex, which requires sensing heavy traffic that needs a large bandwidth. To enhance the reliability of robust cooperative spectrum sensing, the paper proposed a new data fusion scheme based on clustering algorithm and distributed detection, in addition to an adapted threshold based on controlled false alarm probability. The proposed algorithm is dedicated to a highly Rayleigh faded environment to improves the channel errors. The results show that the use of two stages process of distribution clusters and selection fusion node (FN)s gives 0.42 error improvement. The results of the receiver operating characteristic (ROC) curve show an improvement in both false alarms and detection probabilities. Moreover, the sensitivity is also enhanced by 0.95.
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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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The code generated during the current study are available from the corresponding author on reasonable request.
References
Bodart, J., Gishkori, S., Verlant-Chenet, J., Lampe, L., Horlin, F.: Multiband spectrum sensing for cognitive radios based on distributed compressed measurements. In: 2015 IEEE International Conference on Communications (ICC) (2015).
Smith, P.J., Senanayake, R., Dmochowski, P.A., Evans, J.S.: Distributed spectrum sensing for cognitive radio networks based on the sphericity test. IEEE Trans. Commun. 67(3), 1831–1844 (2018)
Anandakumar, H., Umamaheswari, K.: Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Clust. Comput. 20, 1505–1515 (2017)
Karimi, M., Sadough, S.M., Torabi, M.: Optimal cognitive radio spectrum access with joint spectrum sensing and power allocation. IEEE Wirel. Commun. Lett. 9(1), 8–11 (2019)
Deng, M. and Hu, B.J., 2015, September. LLR based spatiotemporal cooperative spectrum sensing schemes for cognitive radios. In: 2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC, 2015). IEEE
Liu, X., Jia, M., Na, Z., Lu, W., Li, F.: Multi-modal cooperative spectrum sensing based on dempster-shafer fusion in 5G-based cognitive radio. IEEE Access. 6, 199–208 (2017)
Digham, F.F., Alouini, M.-S., Simon, M.K.: On the energy detection of unknown signals over fading channels. In: Proc. IEEE ICC, Anchorage, AK, USA, pp. 3575–3579, May 2003.
Yin, W., Chen, H.: Decision-driven time-adaptive spectrum sensing in cognitive radio networks. IEEE Trans. Wirel. Commun. 19(4), 2756–2769 (2020)
Zhang, W., Letaief, K.B.: Cooperative spectrum sensing for cognitive radios under bandwidth constraints. In: IEEE Intern. Wireless Comm. and Netw. Conf. (WCNC2007), pp. 1–5 (2007).
Mokhtar, R.A., Saeed, R.A., Alsaqour, R.A.: Modeling of distributed sensing framework in spectrum aware cognitive radio networks. In: International Review on Computers and Software (IRECOS), Vol. 7 No. 6, pp. 25–31, Italy (2012).
Soni, B., Patel, D.K., Lopez-Benitez, M.: Long short-term memory based spectrum sensing scheme for cognitive radio using primary activity statistics. IEEE Access. 8, 97437–97451 (2020)
Nkalango, S.D., Zhao, H., Song, Y., Zhang, T.: Energy efficiency under double deck relay assistance on cluster cooperative spectrum sensing in hybrid spectrum sharing. IEEE Access. 8, 41298–41308 (2020)
Kim, J., Choi, J.P.: Sensing coverage-based cooperative spectrum detection in cognitive radio networks. IEEE Sens. J. 19(13), 5325–5532 (2019)
Baykas, T., Kasslin, M., Cummings, M., Kang, H., Kwak, J., Paine, R., Reznik, A., Saeed, R., Shellhammer, S.J.: Developing a standard for TV white space coexistence: technical challenges and solution approaches. IEEE Wirel. Commun. 19(1), 10–22 (2012)
Maya, J.A., Vega, L.R., Galarza, C.G.: A locally optimal soft linear-quadratic scheme for CR systems in shadowing environments. IEEE Wirel. Commun. Lett. 5(3), 296–299 (2016)
Awin, F., Abdel-Raheem, E., Tepe, K.: Blind spectrum sensing approaches for interweaved cognitive radio system: a tutorial and short course. IEEE Commun. Surv. Tutorials. 21(1), 238–259 (2018)
Badawy, A., El Shafie, A., Khattab, T.: On the performance of quickest detection spectrum sensing: the case of cumulative sum. IEEE Commun. Lett. 24(4), 739–743 (2020)
Saeed, R.A., Ismail, A.F., Hasan, M.K., Mokhtar, R., Salih, S.K., Hashim, W.: Throughput enhancement for WLAN TV white space in coexistence of IEEE 802.22. Indian J. Sci. Technol. 8(11), 70653 (2015). https://doi.org/10.17485/ijst/2015/v8i11/71783
Saeed R.A., Mokhtar, R.A.: TV white spaces spectrum sensing: recent developments, opportunities and challenges. In: the 6th international conference SETIT 2012: Sciences of Electronic, Technologies of Information and Telecommunications (SETIT2012), pp. 634–638, April 2012, Tunisia.
Nurelmadina, N., Hasan, M.K., Memon, I., Saeed, R.A., Zainol Ariffin, K.A., Ali, E.S., Mokhtar, R.A., Islam, S., Hossain, E., Hassan, M.: A systematic review on cognitive radio in low power wide area network for industrial IoT applications. Sustainability. 13(1), 338 (2021)
Al-Jarrah, M.A., Al-Dweik, A., Ikki, S.S., Alsusa, E.: Spectrum-occupancy aware cooperative spectrum sensing using adaptive detection. IEEE Syst. J. 14(2), 2225–2236 (2019)
Jalali, F., Zaimbashi, A.: Cognitive radio spectrum sensing under joint TX/RX I/Q imbalance and uncalibrated receiver. IEEE Syst. J. 14(1), 105–112 (2019)
Akinbode A. Olawole, Fambirai Takawira, Olutayo O. Oyerinde, “Cooperative Spectrum Sensing in Multichannel Cognitive Radio Networks With Energy Harvesting”, IEEE Access, Volume: 7, 2019.
Kakkavas, G., Tsitseklis, K., Karyotis, V., Papavassiliou, S.: A software defined radio cross-layer resource allocation approach for cognitive radio networks: from theory to practice. IEEE Trans. Cogn. Commun. Netw. 6(2), 740–755 (2020)
Mokhtar, R., Saeed, R.A., Alsaqour, R., Abdallah, Y.: Study on energy detection-based cooperative sensing in cognitive radio networks. J. Netw. (JNW, ISSN 1796–2056) 8(6), 1255–1261. https://doi.org/10.4304/jnw.8.6.1255-1261
Mokhtar, R.A., Khatun, S., Ali, B.M., Saeed, R.A.: Cognitive radio technology for flexible spectrum sharing. In: 2006 4th Student Conference on Research and Development (Vol. Isuue, pp. 44–48). IEEE. SCOReD (2006)
Smith PJ, Senanayake R, Dmochowski PA, Evans JS. Distributed spectrum sensing for cognitive radio networks based on the sphericity test. IEEE Trans. Commun. 201867(3):1831–44.
Aisha, A.H., Saeed, R.A., Hasan, M.K., Islam, S., Khalifa, O.O.: Cluster-based multihop synchronisation scheme for femtocell network. IIUM Eng. J. 13(2), 161–172 (2012)
Cai, P., Zhang, Y.: Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology. Intell. Converg. Netw. 1(1), 79–98 (2020)
Yang, Q., Huang, Y.F., Yen, Y.C., Chen, L.Y., Chen, H.H., Hong, X.M., Shi, J.H., Wang, L.: Location based joint spectrum sensing and radio resource allocation in cognitive radio enabled LTE-U systems. IEEE Trans. Veh. Technol. 69(3), 2967–2979 (2020)
Saeed, R.A., Khatun, S., Ali, B.M., Khazani, M.: Ultra-wideband interference mitigation using cross-layer cognitive radio. In: 2006 IFIP IEEE Conference on Wireless and Optical Communications Networks (WOCN'06), India, Bangalore, 11–13 April 2006.
Yuan, S., Li, L., Chigan, C.: On MMD-based secure fusion strategy for robust cooperative spectrum sensing. IEEE Trans. Cogn. Commun. Network. 5(3), 504–516 (2019)
Li, H., Gu, Y., Chen, J., Pei, Q.: Speed adjustment attack on cooperative sensing in cognitive vehicular networks. IEEE Access. 7, 75925–75934 (2019)
Lee, J., Baek, H., Lim, J.: Cooperative sensing scheme for acquisition of rotational synchronization of radar. IEEE Syst. J. 14(2), 3061–3064 (2019)
Song, I., Kim, D., Lee, S., Yoon, S.: Selection-based detectors and fusion centers for cooperative cognitive radio networks in heavy-tailed noise environment. J. Commun. Netw. 19(3), 259–269 (2017)
Attalla, S.A., Seddik, K.G., El-Sherif, A.A., Rabia, S.I.: Hybrid ARQ-CQI feedback-based access scheme in cognitive radio networks. IEEE Trans. Cogn. Commun. Netw. 6(2), 728–739 (2019)
Bhatnagar, C., Potnis, A., Dwivedy, P., Meena, S.K.: Performance analysis and optimization schemes for cooperative spectrum sensing and information fusion for cognitive radio: a survey. In: 2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech), IEEE (2017)
Tong, J., Jin, M., Guo, Q., Li, Y.: Cooperative spectrum sensing: a blind and soft fusion detector. IEEE Trans. Wirel. Commun. 17(4), 2726–2737 (2018)
Eltom, H., Kandeepan, S., Liang, Y.C., Evans, R.J.: Cooperative soft fusion for HMM-based spectrum occupancy prediction. IEEE Commun. Lett. 22(10), 2144–2147 (2018)
Zhang, S., Wang, Y., Wan, P., Zhuang, J., Zhang, Y., Li, Y.: Clustering algorithm-based data fusion scheme for robust cooperative spectrum sensing. IEEE Access. 8, 5777–5786 (2020)
Azmi, M.H., Leib, H.: Multichannel cooperative spectrum sensing that integrates channel decoding with fusion-based decision. IEEE Trans. Aerospace Electron. Syst. 54(4), 1998–2014 (2018)
Nallagonda, S., Kumar, G.K., Nallagonda, A.K.: Comprehensive performance analysis of data-fusion aided cooperative cognitive radio network over η–μ fading channel. IET Commun. 13(16), 2558–2566 (2019)
Olawole, A.A., Takawira, F., Oyerinde, O.O.: Fusion rule and cluster head selection scheme in cooperative spectrum sensing. IET Commun. 13(6), 758–765 (2019)
Mustafa, R., Jaglan, R.R., Agrawal, S.: Decision-fusion-based reliable CSS scheme in CR networks. IET Commun. 13(7), 947–953 (2019)
Wu, K., Tang, M., Tellambura, C., Ma, D.: Cooperative spectrum sensing as image segmentation: a new data fusion scheme. IEEE Commun. Mag. 56(4), 142–148 (2018)
Verma, P.: Weighted fusion scheme for cooperative spectrum sensing. In: 2020 International Conference on Industry 4.0 Technology (I4Tech) (2020).
Hasan, M.K., Abdalla, A.H., Islam, S., Saeed, R.A.: Intra-cluster synchronization scheme for femtocell networks. In: IEEE International Conference on Computer & Communication Engineering (ICCCE2012), pp 162–168, 3–5 July 2012, Malaysia
Youssef, M.E, Nasim, S., Khisal, U., Khan, A.: Efficient cooperative spectrum detection in cognitive radio systems using wavelet fusion. In: IEEE Inter. Conf. on Comp., Elect. and Elect. Eng. (ICE_Cube2018) (2018).
Sharma, G., Sharma, R.: Performance comparison of hard and soft fusion techniques for energy efficient CSS in cognitive radio. In: IEEE Inter Conf. on Adv. Comp. and Telecomm. (ICACAT2018) (2018).
Varma, A.K., Mitra, D.: A neural network approach to decision fusion for wideband cooperative sensing. In: 2018 Conference on Information and Communication Technology (CICT) (2018).
Verma, G., Dhage, V., Chauhan, S.S.: Analysis of combined data-decision fusion scheme for cognitive radio networks. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC) (2018).
Ayman A. El-Saleh, Mahmoud A.M. Albreem, Tauseef Rasheq Ahad, Waziha Raquib, “Cross entropy algorithm for improved soft fusion-based cooperative spectrum sensing in cognitive radio networks”, IEEE MENA Comm. Conf. (MENACOMM2018), 2018.
Alsolami, F., Alqurashi, F.A., Hasan, M.K., Saeed, R.A., Abdel-Khalek, S., and Ishak, A.B.: Development of Self- Synchronized Drones’ Network using Cluster-based Swarm Intelligence Approach. IEEE Access, Vol. 9 (2021)
Iqbal, Z., Nooshabadi, S., Jadi, K., Ghasemi, A.: Sensor cooperation and decision fusion to improve detection in cognitive radio spectrum sensing. In: 9th IEEE Annual Ubiq. Comp., Elect. & Mobile Comm. Conf. (UEMCON2018) (2018).
Rangel, C.P.M., da Silva Mello, L.A.R.: November. Analysis of performance of fusion rules for cooperative spectrum sensing. In: 2019 IEEE Latin-American Conference on Communications (LATINCOM) (2019)
Hasan, M.K., Saeed, R.A., Abdalla, A.H., Islam, S.: Inter-Cluster Synchronization Scheme For Femtocell Networks. In: IEEE International Conference on Computer & Communication Engineering (ICCCE2012), pp. 147–152, 3–5 July 2012, Malaysia
Balaji, V.: Reinforcement learning based decision fusion scheme for cooperative spectrum sensing in cognitive radios. In: 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (2018).
Junhai, L., Xiaoting, H., Man, W., Yanping, C., Yang, Y.: An optimal bit allocation scheme for cooperative spectrum sensing in cognitive radio networks. In: 2019 22nd International Conference on Information Fusion (FUSION) (2019).
Mohammad, K.H., Saeed, R.A., Aisha, H.A, Shayla, I.: Cluster-based synchronization scheme for femtocell network. In: IEEE International Conference on Computer & Communication Engineering (ICCCE2012), pp. 664–669, 3–5 July 2012, Malaysia
Balam, S.K., Siddaiah, P., Nallagonda, S.: Throughput analysis of cooperative spectrum sensing with hard-decision fusion over generalized κ – μ fading channel. In: IEEE 2nd Inter. Conf. on Adv. in Elec., Comp. and Comm. (ICAECC2018) (2018).
Simpson, O., Sun, Y.: A stochastic based physical layer security in cognitive radio networks: cognitive relay to fusion center. In: IEEE 38th Int. Performance Comp. and Comm. Conf. (IPCCC 2019) (2019).
Elgadi, R., Hilal, A.R., Basir, O.: Intelligent hybrid cooperative spectrum sensing: a multi-stage decision fusion approach. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2017).
de Souza, R.A.A., dos Santos Costa, L., de Almeida, E.M.: A novel decision fusion periodogram-based algorithm for centralized cooperative spectrum sensing under errors at the report channel. In: 2019 13th European Conference on Antennas and Propagation (EuCAP) (2019).
Ahmed, O., Saeed, R.A., Hasan, M.K.: Lightweight inter-cluster synchronization scheme for femtocell network. In: IEEE International Conference on Computing, Electrical and Electronic Engineering (ICCEEE 2013), pp. 229–231, Khartoum, Sudan
Shuai, Y., Lei, L., Chunxiao, C.: Maximum mean discrepancy based secure fusion strategy for robust cooperative spectrum sensing. In: 2018 IEEE International Conference on Communications (ICC) (2018).
Nallagonda, S., Kumar, Y.R., Shilpa, P.: Analysis of hard-decision and soft-data fusion schemes for cooperative spectrum sensing in rayleigh fading channel. In: 2017 IEEE 7th International Advance Computing Conference (IACC) (2017)
Liu, H., Zhu, X., Fujii, T.: Ensemble deep learning-based cooperative spectrum sensing with semi-soft stacking fusion center. In: 2019 IEEE Wireless Communications and Networking Conference (WCNC) (2019).
Liu, H., Zhu, X., Fujii, T.: Ensemble deep learning based cooperative spectrum sensing with stacking fusion center. In: 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE (2018)
Ruby, D., Vijayalakshmi, M., Kannan, A.: Intelligent relay selection and spectrum sharing techniques for cognitive radio networks. Clust. Comput. 22, 10537–10548 (2019)
Awasthi, M., Madhav, J.N., Kumar, V.: Energy Efficient Hard Decision Fusion Rules For Fading And Non-Fading Environment. In: 2017 IEEE Region 10 Conference (TENCON) (2017).
Funding
This research was supported by Taif University Researchers Supporting Project number (TURSP-2020/216), Taif University, Taif, Saudi Arabia.
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Conceptualization, RAM, RAS. Data curation, HA, RAS, SA-K. Formal analysis, RAM, HA. Funding acquisition, RAS, HA, SA-K. The investigation, All Authors. Methodology, RAM, RAS, MK. Project administration, All Authors. Resources, RAM, RAS, SA-K. Software, all authors. Supervision, RAS, HA. Validation, HA, RAS, SA-K. Visualization, RAM, RAS, MK. Writing—original draft, all authors. Writing—review editing—all authors.
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Mokhtar, R.A., Saeed, R.A., Alhumyani, H. et al. Cluster mechanism for sensing data report using robust collaborative distributed spectrum sensing. Cluster Comput 25, 2541–2556 (2022). https://doi.org/10.1007/s10586-021-03363-8
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DOI: https://doi.org/10.1007/s10586-021-03363-8