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 - 15 December 2023

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… More > Graphic Abstract

Noor Gul^1,2, Saeed Ahmed^1,3, Su Min Kim¹, Muhammad Sajjad Khan⁴, Junsu Kim^1,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5209-5227, 2023, DOI:10.32604/cmc.2023.026945 - 28 December 2022

Abstract A cognitive radio network (CRN) intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum utilization. In CRNs, the secondary users (SUs) opportunistically access the primary users (PUs) spectrum. Therefore, unambiguous detection of the PU channel occupancy is the most critical aspect of the operations of CRNs. Cooperative spectrum sensing (CSS) is rated as the best choice for making reliable sensing decisions. This paper employs machine-learning tools to sense the PU channels reliably in CSS. The sensing parameters are reconfigured to maximize the spectrum utilization while reducing sensing 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 - 16 August 2022

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

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Ramesh Ramamoorthy¹, Himanshu Sharma², A. Akilandeswari³, Nidhi Gour², Arun Kumar^4,*, Mehedi Masud⁵

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1207-1217, 2022, DOI:10.32604/csse.2022.024749 - 09 May 2022

Abstract Spectrum sensing is one of the major concerns in reaching an efficient Quality of service (QOS) in the advanced mobile communication system. The advanced engineering sciences such as 5G, device 2 device communications (D2D), Internet of things (IoT), MIMO require a large spectrum for better service. Orthogonal frequency division multiplexing (OFDM) is not a choice in advanced radio due to the Cyclic Prefix (CP), wastage of the spectrum, and so on. Hence, it is important to explore the spectral efficient advanced waveform techniques and combine a cognitive radio (CR) with the 5G waveform to sense… More >