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  • Open Access


    Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

    Shayla Islam1, Anil Kumar Budati1,*, Mohammad Kamrul Hasan2, Saoucene Mahfoudh3, Syed Bilal Hussian Shah3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 813-825, 2023, DOI:10.32604/cmes.2023.027595

    Abstract In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promising approach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio (SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambient Radio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum for the transmission of data without loss or without collision at a specific time. In this paper, the authors proposed a novel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing… More >

  • Open Access


    Spectrum Sensing Using Optimized Deep Learning Techniques in Reconfigurable Embedded Systems

    Priyesh Kumar*, Ponniyin Selvan

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2041-2054, 2023, DOI:10.32604/iasc.2023.030291

    Abstract The exponential growth of Internet of Things (IoT) and 5G networks has resulted in maximum users, and the role of cognitive radio has become pivotal in handling the crowded users. In this scenario, cognitive radio techniques such as spectrum sensing, spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication. IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index, frequency bands, coding rate etc., to accommodate the above characteristics. Implementing the above learning methods on the embedded chip leads… More >

  • Open Access


    Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

    G. Arunachalam1,*, P. SureshKumar2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1279-1294, 2023, DOI:10.32604/csse.2023.031001

    Abstract The main components of Cognitive Radio networks are Primary Users (PU) and Secondary Users (SU). The most essential method used in Cognitive networks is Spectrum Sensing, which detects the spectrum band and opportunistically accesses the free white areas for different users. Exploiting the free spaces helps to increase the spectrum efficiency. But the existing spectrum sensing techniques such as energy detectors, cyclo-stationary detectors suffer from various problems such as complexity, non-responsive behaviors under low Signal to Noise Ratio (SNR) and computational overhead, which affects the performance of the sensing accuracy. Many algorithms such as Long-Short Term Memory (LSTM), Convolutional Neural… More >

  • Open Access


    A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

    Nada M. Elfatih1, Elmustafa Sayed Ali1,5, Maha Abdelhaq2, Raed Alsaqour3,*, Rashid A. Saeed4

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 329-342, 2023, DOI:10.32604/csse.2023.028528


    In cognitive radio networks (CoR), the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability. Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection. However, these methods do not take into account the effect of sample size and its effect on improving CoR performance. In general, a large sample size results in more reliable detection, but takes longer sensing time and increases complexity. Thus, the locally sensed sample size is an optimization problem. Therefore, optimizing the local sample size for each cognitive… More >

  • Open Access


    Throughput Enhancement for NOMA Systems Using Intelligent Reflecting Surfaces

    Raed Alhamad1,*, Hatem Boujemaa2

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5233-5244, 2022, DOI:10.32604/cmc.2022.030793

    Abstract In this article, we optimize the powers associated to Non Orthogonal Multiple Access (NOMA) users, sensing and harvesting duration for Cognitive Radio Networks (CRN). The secondary source harvests energy from node A signal. Then, it senses the channel to detect primary source. Then, the secondary source transmits a signal that is reflected by Intelligent Reflecting Surfaces (IRS) so that all reflections have a zero phase at any user. A set Ii of reflectors are associated to user Ui. The use of M = Mi = 512, 256, 128, 64, 32, 16, 8 reflectors per user offers 45, 42, 39, 36, 33, 30, 27 dB gain… More >

  • Open Access


    Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

    P. Gnanasivam1, G. T. Bharathy1,*, V. Rajendran2, T. Tamilselvi1

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 55-65, 2023, DOI:10.32604/csse.2023.023374

    Abstract Wireless Communication is a system for communicating information from one point to other, without utilizing any connections like wire, cable, or other physical medium. Cognitive Radio (CR) based systems and networks are a revolutionary new perception in wireless communications. Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum. Centralized Cooperative Spectrum Sensing (CSS) is a kind of spectrum sensing. Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix… More >

  • Open Access


    A Neuro Fuzzy with Improved GA for Collaborative Spectrum Sensing in CRN

    S. Velmurugan1,*, P. Ezhumalai2, E. A. Mary Anita3

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1093-1108, 2022, DOI:10.32604/iasc.2022.026308

    Abstract Cognitive Radio Networks (CRN) have recently emerged as an important solution for addressing spectrum constraint and meeting the stringent criteria of future wireless communication. Collaborative spectrum sensing is incorporated in CRNs for proper channel selection since spectrum sensing is a critical capability of CRNs. According to this viewpoint, this study introduces a new Adaptive Neuro Fuzzy logic with Improved Genetic Algorithm based Channel Selection (ANFIGA-CS) technique for collaborative spectrum sensing in CRN. The suggested method’s purpose is to find the best transmission channel. To reduce spectrum sensing error, the suggested ANFIGA-CS model employs a clustering technique. The Adaptive Neuro Fuzzy… More >

  • Open Access


    Cognitive Radio Networks Using Intelligent Reflecting Surfaces

    Raed Alhamad*

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 751-765, 2022, DOI:10.32604/csse.2022.021932

    Abstract In this article, we optimize harvesting and sensing duration for Cognitive Radio Networks (CRN) using Intelligent Reflecting Surfaces (IRS). The secondary source harvests energy using the received signal from node A. Then, it performs spectrum sensing to detect Primary Source PS activity. When PS activity is not detected, The Secondary Source SS transmits data to Secondary Destination SD where all reflected signals on IRS are in phase at SD. We show that IRS offers 14, 20, 26, 32, 38, 44, 50 dB enhancement in throughput using M = 8, 16, 32, 64, 128, 256, 512 reflectors with respect to CRN without IRS. We… More >

  • Open Access


    Spectral Vacancy Prediction Using Time Series Forecasting for Cognitive Radio Applications

    Vineetha Mathai*, P. Indumathi

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1729-1746, 2022, DOI:10.32604/iasc.2022.024234

    Abstract An identification of unfilled primary user spectrum using a novel method is presented in this paper. Cooperation among users with the utilization of machine learning methods is analyzed. Learning methods are applied to construct the classifier, which selects the suitable fusion algorithm for the considered environment so that the out of band sensing is performed efficiently. Sensing performance is looked into with the existence of fading and it is observed that sensing performance degrades with fading which coincides with earlier findings. From the simulation, it can be inferred that Weibull fading outperforms all the other fading models considered. To accomplish… More >

  • Open Access


    Spectrum Prediction in Cognitive Radio Network Using Machine Learning Techniques

    D. Arivudainambi1, S. Mangairkarasi1,*, K. A. Varun Kumar2

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1525-1540, 2022, DOI:10.32604/iasc.2022.020463

    Abstract Cognitive Radio (CR) aims to achieve efficient utilization of scarcely available radio spectrum. Spectrum sensing in CR is a basic process for identifying the existence or absence of primary users. In spectrum sensing, CR users suffer from deep fading effects and it requires additional sensing time to identify the primary user. To overcome these challenges, we frame Spectrum Prediction-Channel Allocation (SP-CA) algorithm which consists of three phases. First, clustering mechanisms to select the spectrum coordinator. Second, Eigenvalue based detection method to expand the sensing accuracy of the secondary user. Third, Bayesian inference approach to reduce the performance degradation of the… More >

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