Deep Learning Aided Multi-Level Transmit Power Recognition in Cognitive Radio Networks

Tan, Zhenyu; Wang, Danyang; Liu, Qi; Li, Zan; Zhang, Ning; Abdel-Raheem, Esam

doi:10.1109/tccn.2023.3235738

Cited by 1 publication

(1 citation statement)

References 49 publications

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“…A ResNet-based multi-level transmission power recognition (MTPR) design has been described by Tan et al [17] Also, the suggested architecture was used in two instances with various observational information. In the initial instance, the covariance matrix (CM) of the received signal, which contains extensive energy data, was employed as the comment information for the CM-MTPR technique.…”

Section: Related Workmentioning

confidence: 99%

Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

Saravanan,

Muthaiah,

Rajesh

2024

CMES

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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 methodology is a combination of dynamic threshold detection and MBWO. The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal. These limitations undermine the sensing accuracy of the energy identification technique. Hence, the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks. The projected approach is executed and analyzed with performance and comparison analysis. The proposed method is contrasted with the conventional techniques of the Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO). It indicated superior results, achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8, outperforming conventional techniques. KEYWORDSCognitive radio network; spectrum sensing; noise uncertainty; modified black widow optimization algorithm; energy detection technique Abbreviation Λ slc Complete test statistics of complete signals identified based on receiver chains of the secondary user Λ R Test statistics for signals identified at the receiver chain of the secondary chain n Complete number of secondary users of receiver chains r Count of secondary users in receiver chains

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Section: Related Workmentioning

confidence: 99%