Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

Al‐Saggaf, Ubaid M.; Jafarian, Ahmad; Alrefaei, Mohammed A.; Moinuddin, Muhammad

doi:10.3390/math11163533

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…In contrast, MSA-based approaches present a promising solution by integrating natureinspired search operators, facilitating the discovery of optimal network architectures without the need for specialized domain expertise. These methods, including particle swarm optimization (PSO), grey wolf optimization (GWO), teaching-learning-based optimization (TLBO), and differential evolution (DE), exhibit robust global search capabilities and find extensive application across various domains [21][22][23][24]. Due to their appealing features, MSA-based techniques have emerged as popular alternatives to conventional design methods, offering researchers a versatile tool to effectively address a wide array of deep learning challenges.…”

Section: Recent Progress In Network Architecture Design Techniquesmentioning

confidence: 99%

MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

Ang,

Lim,

Tiang

et al. 2023

Mathematics

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Convolutional neural networks (CNNs) have excelled in artificial intelligence, particularly in image-related tasks such as classification and object recognition. However, manually designing CNN architectures demands significant domain expertise and involves time-consuming trial-and-error processes, along with substantial computational resources. To overcome this challenge, an automated network design method known as Modified Teaching-Learning-Based Optimization with Refined Knowledge Sharing (MTLBORKS-CNN) is introduced. It autonomously searches for optimal CNN architectures, achieving high classification performance on specific datasets without human intervention. MTLBORKS-CNN incorporates four key features. It employs an effective encoding scheme for various network hyperparameters, facilitating the search for innovative and valid network architectures. During the modified teacher phase, it leverages a social learning concept to calculate unique exemplars that effectively guide learners while preserving diversity. In the modified learner phase, self-learning and adaptive peer learning are incorporated to enhance knowledge acquisition of learners during CNN architecture optimization. Finally, MTLBORKS-CNN employs a dual-criterion selection scheme, considering both fitness and diversity, to determine the survival of learners in subsequent generations. MTLBORKS-CNN is rigorously evaluated across nine image datasets and compared with state-of-the-art methods. The results consistently demonstrate MTLBORKS-CNN’s superiority in terms of classification accuracy and network complexity, suggesting its potential for infrastructural development of smart devices.

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Section: Recent Progress In Network Architecture Design Techniquesmentioning

confidence: 99%

MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

Ang,

Lim,

Tiang

et al. 2023

Mathematics

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Improved energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor networks

Sharada,

Mahesh,

chandrasekaran

et al. 2024

Sci Rep

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Optimization algorithms have come a long way in the last several decades, with the goal of reducing energy consumption and minimizing interference with primary users during data transmission over shorter distances. The adaptive ant colony distributed intelligent based clustering algorithm (AACDIC) is a key component of the cognitive radio (CR) system because of its superior performance in spectrum sensing among a group of multi-users in terms of reduced sensing errors, power conservation, and faster convergence times. This study presents the AACDIC method, which improves energy efficiency by determining the ideal cluster count using connectedness and distributed cluster-based sensing. In this study, we take into account the reality of a system with an unpredictable number of both primary users and secondary users. As a result, the proposed AACDIC method outperforms pre-existing optimization algorithms by increasing the rate at which solutions converge via the utilisation of multi-user clustered communication. Experiments show that compared to other algorithms, the AACDIC method significantly reduces node power usage by 9.646 percent. The average power of Secondary Users nodes is reduced by 24.23 percent compared to earlier versions. The AACDIC algorithm is particularly strong at reducing the Signal-to-Noise Ratio to levels as low as 2 dB, which significantly increases the likelihood of detection. When comparing AACDIC to other primary detection optimization strategies, it is clear that it has the lowest false positive rate. The proposed AACDIC algorithm optimizes network capacity performance, as shown by the results of simulations, due to its ability to solve multimodal optimization challenges. Our analysis reveals that variations in SNR significantly affect the probability of successful detection, shedding light on the intricate interplay between signal strength, noise levels, and the overall reliability of sensor data. This insight contributes to a more comprehensive understanding of the proposed scheme's performance in realistic deployment scenarios, where environmental conditions may vary dynamically. The experimental results demonstrate the effectiveness of the proposed algorithm in mitigating the identified drawback and highlight the importance of SNR considerations in optimizing detection reliability in energy-constrained WSNs.

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Cooperrative Spectrum Sensing in Cognitive Radio Network Using Flying Fox Optimization Based AlexNet

Devi,

Al-Farouni,

Pradeep Kumar

et al. 2024

2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON)

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Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

Cited by 3 publications

References 48 publications

MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

Improved energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor networks

Cooperrative Spectrum Sensing in Cognitive Radio Network Using Flying Fox Optimization Based AlexNet

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