Improving Spectrum Sensing for Cognitive Radio Network Using the Energy Detection with Entropy Method

Usman, Mustefa Badri; Singh, Ram Sewak; Mishra, Satyasis; Rathee, Davinder

doi:10.1155/2022/2656797

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…Narrow-band methods, such as energy detection and matching filters, are not very flexible in a spectrum that is continually evolving and changing and require some understanding of the transmission to find holes efficiently [19]. Narrowband approaches such as adaptive thresholds, cyclo-stationary feature extraction, and a few deep learning and machine learning methods work well in low SNR, but they are not excellent because they must scan the spectrum sequentially to get a sense of dwellings for the wideband, which is time-consuming and may miss holes [20,21]. In spectrum-sharing applications, wideband sensing methods are preferable because they can track several holes simultaneously and distribute them sensibly.…”

Section: Literature Reviewmentioning

confidence: 99%

Advancing Wideband Spectrum Sensing in Cognitive Radio Networks Using a Deep Learning Approach

2023

Journal of Southwest Jiaotong University

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A novel spectrum sensing method is proposed for application in cognitive radio networks (CRNs). Wideband spectrum sensing is essential for locating and capitalizing on spectral holes, thereby facilitating effective spectrum use. However, problems such as noise uncertainty and signal-to-noise ratio (SNR) variations typically plague conventional sensing methods, resulting in less-than-ideal performance. Here, we employ convolutional neural networks (CNNs) to automatically acquire and extract important spectral features from raw spectrum data. The MATLAB-generated dataset of spectrum measurements was used to fine-tune the CNN model's ability to detect and categorize spectrum holes with more precise SNR estimates. The findings show that the deep learning-driven wideband spectrum sensing method is better than traditional approaches. CNN-based sensing efficiently characterizes spectrum utilization and improves detection of primary user (PU) activities. The proposed solution significantly improves the spectrum efficiency and resilience of CRNs by harnessing the power of ML, which leads to more effective resource utilization and less interference. Simulation results show that our proposed strategy produces more accurate spectrum occupancy assessments than existing methods, with a validation accuracy of 82.4%. This study introduces a cutting-edge deep learning model for wideband spectrum sensing in CRNs. Our deep learning methodology uses convolutional neural networks to automatically learn and adapt to dynamic and complicated radio environments, improving accuracy and flexibility over classic spectrum sensing approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Advancing Wideband Spectrum Sensing in Cognitive Radio Networks Using a Deep Learning Approach

2023

Journal of Southwest Jiaotong University

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Improved gradient boosting hybrid spectrum sharing and actor critic channel allocation in 6G CR-IOT

Kothari,

Kurumbanshi

2024

Int. j. inf. tecnol.

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Efficient improvement of energy detection technique in cognitive radio networks using K-nearest neighbour (KNN) algorithm

Musuvathi,

Archbald,

Velmurugan

et al. 2024

J Wireless Com Network

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With the birth of the IoT era, it is evident that the existing number of devices is going to rise exponentially. Any two devices will communicate with each other using the same frequency band with limited availability. Therefore, it is of vital importance that this frequency band used for communication be used efficiently to accommodate the maximum number of devices with the available radio resources. Cognitive radio (CR) technology serves this exact purpose. The stated one is an intelligent radio that is made to automatically identify the optimal wireless channel in the available wireless spectrum at a given instant. An important functionality of CR is spectrum sensing. Energy detection is a very popular algorithm used for spectrum sensing in CR technology for efficient allocation of radio resources to the devices intended to communicate with each other. Energy detection detects the presence of a primary user (PU) signal by continuously monitoring a selected frequency bandwidth. The conventional energy detection technique is known to perform poorly in lower SNR ranges. This paper works towards the improvement of the energy detection algorithm with the help of machine learning (ML). The ML model uses the general properties of the signal as training data and classifies between a PU signal and noise at very low SNR ranges (− 25 to − 10 dB). In this research, a K-nearest neighbours (KNN) model is selected for its versatility and simplicity. Upon testing the model with an out-of-sample dataset, the KNN model produced a detection accuracy of 94.5%.

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Improving Spectrum Sensing for Cognitive Radio Network Using the Energy Detection with Entropy Method

Cited by 5 publications

References 34 publications

Advancing Wideband Spectrum Sensing in Cognitive Radio Networks Using a Deep Learning Approach

Advancing Wideband Spectrum Sensing in Cognitive Radio Networks Using a Deep Learning Approach

Improved gradient boosting hybrid spectrum sharing and actor critic channel allocation in 6G CR-IOT

Efficient improvement of energy detection technique in cognitive radio networks using K-nearest neighbour (KNN) algorithm

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