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An Efficient Cooperative Spectrum Sensing Method Using Renyi Entropy Weighted Optimal Likelihood Ratio for CRN

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main concept behind employing cognitive radio is to enable secondary users (SUs) or unlicensed users to utilize the available spectrum. Spectrum sensing methods detect the existence of primary users (PUs) and have become the main topic of research in the CRN industry and in academia. This paper proposes a new framework based on the Adam gradient descent (Adam GD) algorithm to develop a spectrum sensing mechanism used in CRNs and detecting the availability of free channels. The signal's components are extracted from the received signal and the spectrum is searched for availability which is detected through a fusion center using the proposed algorithm. The proposed Adam GD algorithm attains the maximum detection probability rate and the minimum false alarm probability of 0.71 and 0.39, respectively, for a Rayleigh channel.
Rocznik
Tom
Strony
41--48
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
  • Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India
  • Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India
Bibliografia
  • [1] M. Amjad, M.H. Rehmani, and S. Mao, "Wireless Multimedia Cognitive Radio Networks: A Comprehensive Survey", IEEE Communications Surveys & Tutorials, vol. 20, no. 2, pp. 1056-1103, 2018.
  • [2] Y. Chen and H.S. Oh, "A Survey of Measurement-based Spectrum Occupancy Modeling for Cognitive Radios", IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 848-859, 2014.
  • [3] J. Lunden, V. Koivunen, and H.V. Poor, "Spectrum Exploration and Exploitation for Cognitive Radio: Recent Advances", IEEE Signal Processing Magazine, vol. 32, no. 3, pp. 123-140, 2015.
  • [4] C. Liu, J. Wang, X. Liu, and Y.C. Liang, "Deep CM-CNN for Spectrum Sensing in Cognitive Radio", IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2306-2321, 2019.
  • [5] Y. Ma, Y. Gao, Y.C. Liang, and S. Cui, "Reliable and Efficient Sub-Nyquist Wideband Spectrum Sensing in Cooperative Cognitive Radio Networks", IEEE Journal on Selected Areas in Communications, vol. 34, no. 10, pp. 2750-2762, 2016.
  • [6] G. Eappen and T. Shankar, "Multi-Objective Modified Grey Wolf Optimization Algorithm for Efficient Spectrum Sensing in the Cognitive Radio Network", Arabian Journal for Science and Engineering, vol. 46, no. 4, pp. 3115-3145, 2021.
  • [7] G. Eappen and T. Shankar, "Hybrid PSO-GSA for energy efficient spectrum sensing in cognitive radio network", Physical Communication, vol. 40, art. no. 101091, 2020.
  • [8] H.A. Shah and I. Koo, "Reliable machine learning based spectrum sensing in cognitive radio networks", Wireless Communications and Mobile Computing, vol. 2018, art. no. 5906097, 2018.
  • [9] H. He and H. Jiang, "Deep Learning Based Energy Efficiency Optimization for Distributed Cooperative Spectrum Sensing", IEEE Wireless Communications, vol. 26, no. 3, pp. 32-39, 2019.
  • [10] Y. Arjoune and N. Kaabouch, "A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions", Sensors, vol. 19, no. 1, art. no. 126, 2019.
  • [11] Y. Zeng and Y.C. Liang, "Robustness of the Cyclostationary Detection to Cyclic Frequency Mismatch", in 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Istanbul, Turkey, pp. 2704-2709, 2010.
  • [12] A. Taherpour, M. Nasiri-Kenari, and S. Gazor, "Multiple Antenna Spectrum Sensing in Cognitive Radios", IEEE Transactions on Wireless Communications, vol. 9, no. 2, pp. 814-823, 2010.
  • [13] H.S. Chen, W. Gao, and D.G. Daut, "Signature Based Spectrum Sensing Algorithms for IEEE 802.22 WRAN", in IEEE International Conference on Communications, Glasgow, UK, pp. 6487-6492, 2007.
  • [14] C. Liu, J. Wang, X. Liu, and Y.C. Liang, "Maximum Eigenvalue-Based Goodness-of-Fit Detection for Spectrum Sensing in Cognitive Radio", IEEE Transactions on Vehicular Technology, vol. 68, no. 8, pp. 7747-7760, 2019.
  • [15] E. Soltanmohammadi, M. Orooji, and M. Naraghi-Pour, "Spectrum Sensing over MIMO Channels Using Generalized Likelihood Ratio Tests", IEEE Signal Processing Letters, vol. 20, no. 5, pp. 439-442, 2013.
  • [16] J. Zhang et al., "MIMO Spectrum Sensing for Cognitive Radio-Based Internet of Things", IEEE IoT Journal, vol. 7, no. 9, pp. 8874-8885, 2020.
  • [17] F. Azmat, Y. Chen, and N. Stocks, "Analysis of Spectrum Occupancy Using Machine Learning Algorithms", IEEE Transactions on Vehicular Technology, vol. 65, no. 9, pp. 6853-6860, 2015.
  • [18] S. Zheng et al., "Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios", China Comm., vol. 17, no. 2, pp. 138-148, 2020.
  • [19] W.M. Lees et al., "Deep Learning Classification of 3.5-GHz Band Spectrograms with Applications to Spectrum Sensing", IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 2, pp. 224-236, 2019.
  • [20] J. Xie, J. Fang, C. Liu, and X. Li, "Deep Learning-Based Spectrum Sensing in Cognitive Radio: A CNN-LSTM Approach", IEEE Communications Letters, vol. 24, no. 10, pp. 2196-2200, 2020.
  • [21] A. Kaur and K. Kumar, "Imperfect CSI Based Intelligent Dynamic Spectrum Management Using Cooperative Reinforcement Learning Framework in Cognitive Radio Networks", IEEE Transactions on Mobile Computing, vol. 27 no. 5, pp. 1672-1683, 2020.
  • [22] G. Pan, J. Li, and F. Lin, "A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum", International Journal of Digital Multimedia Broadcasting, art. no. 5069021, 2020.
  • [23] S. Jothiraj, S. Balu, and N. Rangaraj, "An efficient adaptive threshold‐based dragonfly optimization model for cooperative spectrum sensing in cognitive radio networks", International Journal of Communication Systems, vol. 34, no. 10, art. no. e4829, 2021.
  • [24] S.S. Reddy and M.S.G. Prasad, "Improved Whale Optimization Algorithm and Convolutional Neural Network Based Cooperative Spectrum Sensing in Cognitive Radio Networks", Information Security Journal: A Global Perspective, vol. 30, no. 3, pp. 160-172, 2021.
  • [25] A, Patel, H. Ram, A.K. Jagannatham, and P.K. Varshney, "Robust Cooperative Spectrum Sensing for MIMO Cognitive Radio Networks under CSI Uncertainty", IEEE Transactions on Signal Processing, vol. 66, no. 1, pp. 18-33, 2017.
  • [26] K.U. Chowdary and B.P. Rao, "Hybrid mixture model based on a hybrid optimization for spectrum sensing to improve the performance of MIMO–OFDM systems", International Journal of Pattern Recognition and Artificial Intelligence, vol. 34, no. 7, art. no. 2058008, 2020.
  • [27] S. Ruder, "An overview of gradient descent optimization algorithms", 2016.
  • [28] D.P. Kingma and J. Ba, "Adam: A method for stochastic optimization", 3rd Int. Conference for Learning Representations, San Diego, USA, 2014.
  • [29] S. Ali, G. Seco-Granados, and J.A. López-Salcedo, "Spectrum Sensing with Spatial Signatures in the Presence of Noise Uncertainty and Shadowing", EURASIP Journal on Wireless Comm. and Netw., art. no. 150, pp. 1-16, 2013.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e81aa193-07a2-41d7-83e5-41ffa4c8f419
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