Autors: Ivanov, A. S., Mihovska, A., Tonchev, K., Poulkov, V. K.
Title: Real-time adaptive spectrum sensing for cyclostationary and energy detectors
Keywords: Detectors; Signal to noise ratio; Cognitive radio; Interfere

Abstract: Multiple spectrum measurement campaigns around the world have shown that even though the frequencies below 6 GHz are very crowded, enough portions of the spectrum remain unutilized most of the time. Even the most heavily employed bands, such as the ones used for television broadcasting and cellular communications, have shown an average occupancy between 25 and 50% [1]. Together with the ever-increasing growth of connected devices in the scope of the Internet of Things (IoT) concept, these have been the main motivational factors for the intensive research in the field of cognitive radio (CR), which empowered by the abilities of the software-defined radio (SDR) devices, can enable the delivery of IoT services, as they operate together with the incumbent users of the existing wireless networks. Consequently, the CR can be utilized for a variety of applications within IoT and the modern systems for delivery of intelligent services, or the traditional standards for wireless data ..



    IEEE Aerospace and Electronic Systems Magazine, vol. 33, issue 5, pp. 20-33, 2018, United States, IEEE, DOI 10.1109/MAES.2018.170098

    Copyright IEEE

    Цитирания (Citation/s):
    1. Yang, J.X., Ser, W., Lei, L., "Multiple Cycle Frequencies Estimation Under Cochannel Interference", IEEE Signal Processing Letters, vol. 25, no. 11, pp. 1645-1649, 2018, DOI: 10.1109/LSP.2018.2867730. - 2018 - в издания, индексирани в Scopus или Web of Science
    2. Napolitano, A., "Cyclostationary processes and time series: Theory, applications, and generalizations", Cyclostationary Processes and Time Series: Theory, Applications, and Generalizations, pp. 1-626, 2019, DOI: 10.1016/C2017-0-04240-4. - 2019 - в издания, индексирани в Scopus или Web of Science
    3. Bhowmik, M., Malathi, P., "Spectrum Sensing in Cognitive Radio Using Actor–Critic Neural Network with Krill Herd-Whale Optimization Algorithm", Wireless Personal Communications, vol. 105, no. 1, pp. 335-354, 2019, DOI: 10.1007/s11277-018-6115-5. - 2019 - в издания, индексирани в Scopus или Web of Science
    4. Xu, Z., Petrunin, I., Tsourdos, A., "Identification of Communication Signals Using Learning Approaches for Cognitive Radio Applications", IEEE Access, vol. 8, pp. 128930-128941, 2020, DOI: 10.1109/ACCESS.2020.3009181. - 2020 - в издания, индексирани в Scopus или Web of Science
    5. Suguna, R., Rathinasabapathy, V., "Hybrid spectrum sensing architecture using LLCBC MAC for CR-WSN applications", Analog Integrated Circuits and Signal Processing, vol. 108, no. 3, pp. 657-669, 2021, DOI: 10.1007/s10470-021-01848-5. - 2021 - в издания, индексирани в Scopus или Web of Science
    6. Xu, M., Yin, Z., Zhao, Y., Wu, Z., "Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network", Entropy, vol. 24, no. 1, 2022, DOI: 10.3390/e24010129. - 2022 - в издания, индексирани в Scopus или Web of Science
    7. Manco, J., Dayoub, I., Nafkha, A., Alibakhshikenari, M., Thameur, H.B., "Spectrum Sensing Using Software Defined Radio for Cognitive Radio Networks: A Survey", IEEE Access, vol. 10, pp. 131887-131908, 2022, DOI: 10.1109/ACCESS.2022.3229739. - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science