R. Saravanan^1,*, R. Muthaiah¹, A. Rajesh²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2339-2356, 2024, DOI:10.32604/cmes.2023.030898

Abstract This study develops an Enhanced Threshold Based Energy Detection approach (ETBED) for spectrum sensing in a cognitive radio network. The threshold identification method is implemented in the received signal at the secondary user based on the square law. The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing. Additionally, the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems. In the dynamic threshold, the signal ratio-based threshold is fixed. The threshold is computed by considering the Modified Black Widow Optimization Algorithm (MBWO). So, the proposed… More > Graphic Abstract

Shayla Islam¹, Anil Kumar Budati^1,*, 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 >

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 >

G. Arunachalam^1,*, 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 >

Nada M. Elfatih¹, Elmustafa Sayed Ali^1,5, Maha Abdelhaq², Raed Alsaqour^3,*, Rashid A. Saeed⁴

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

Abstract

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 >

Raed Alhamad^1,*, 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 >

P. Gnanasivam¹, G. T. Bharathy^1,*, 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 >

S. Velmurugan^1,*, 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 >

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 P_S activity. When P_S activity is not detected, The Secondary Source S_S transmits data to Secondary Destination S_D where all reflected signals on IRS are in phase at S_D. 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 >

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 >