Optimized Spectrum Sensing Algorithm for Cognitive Radio

Karthikeyan, C.; Suganthi, M.

doi:10.1007/s11277-016-3642-9

Cited by 11 publications

(10 citation statements)

References 27 publications

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“…Additionally, the comparative analysis also provided to know the efficiency of the AAS-WTM-LFA based spectrum sensing in CR. The existing methods used for the comparison are ACS [18], AAS [18], AAS-CSLA [22], AAS-R8-CSA [22] and AAS-R8-CSLA [22]. The implementation of AAS-WTM-LFA architecture is carried out in the Xilinx tool for various virtex devices.…”

Section: Resultsmentioning

confidence: 99%

“…The implementation of AAS-WTM-LFA architecture is carried out in the Xilinx tool for various virtex devices. Here, ACS [18], AAS [18], AAS-CSLA [22], AAS-R8-CSA [22] and AAS-R8-CSLA [22] are implemented by using Xilinx tool to evaluate the AAS-WTM-LFA architecture.…”

Section: Resultsmentioning

confidence: 99%

“…C.S. Karthikeyan and M. Suganthi [18] presented the adaptive self-coherent-restoral (SCORE) based spectrum sensing method for CR. Initially, the desired signal is achieved by using the Adaptive Absolute-SCORE (AAS) algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

“…The performances of AAS-WTM-LFA is compared with five different architectures in terms of FPGA parameters. The five different architectures used for the comparison are ACS [18], AAS [18], AAS-CSLA [22], AAS-R8-CSA [22] and AAS-R8-CSLA [22].…”

Section: Performance Analysis Using Xilinx Toolmentioning

confidence: 99%

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FPGA Implementation of Adaptive Absolute SCORE Algorithm for Cognitive Radio Spectrum Sensing with WTM and LFA

Kumar¹,

Bikshalu²

2021

IJIES

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Cognitive Radio (CR) is generally a wireless communication system that has the ability to improve the network’s system-capacity. Since, the white space or temporally unused spectrum are used to enhance the systemcapacity and the important operation involved in the cognition cycle is spectrum sensing. This spectrum sensing supports the Cognitive Radio users to adjust with the environment by identifying the white/vacant spaces without creating any interference to the primary user communication. The traditional filters such as Finite Impulse Response (FIR) filters and median filters used in the spectrum sensing obtains high area utilization in Cognitive Radio. In order to overcome this, an Adaptive Absolute SCORE (AAS) technique is developed based on the FIR for improving the sensing function and radio sensitivity. The area and frequency of the AAS are enhanced by using the Wallace tree multiplier (WTM) and Ladner-Fischer Adder (LFA) in the design of the FIR. The proposed architecture used for the spectrum sensing is named as AAS-WTM-LFA. This AAS-WTM-LFA architecture is developed in the Xilinx tool for different Virtex devices. The performance of AAS-WTM-LFA is analyzed in terms of LUT, slices, flip flops, bonded Input and Output Block (IOB), frequency and power. Additionally, the quality of signal processed through the AAS-WTM-LFA architecture is analyzed as Bit Error Rate (BER) and False Acceptance Rate (FAR). Additionally, the AAS-WTM-LFA architecture is compared with ACS, AAS, AAS-CSLA, AAS-R8-CSA and AASR8-CSLA. The number of LUT for AAS-WTM-LFA architecture is 247 for Spartan 6 device, that is less when compared to the remaining architectures.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Performance Analysis Using Xilinx Toolmentioning

confidence: 99%

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FPGA Implementation of Adaptive Absolute SCORE Algorithm for Cognitive Radio Spectrum Sensing with WTM and LFA

Kumar¹,

Bikshalu²

2021

IJIES

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“…As the core technology of the cognitive radio network (CRN), spectrum sensing can improve spectrum utilization and alleviate spectrum resources [8][9][10]. Traditional spectrum sensing is mainly done by mathematical methods of signal processing, including energy detection [11], matched filter detection [12], and cyclostationary feature detection [13].…”

Section: Introductionmentioning

confidence: 99%

A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

Pan

Lin

2020

International Journal of Digital Multimedia Broadcasting

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In a cognitive radio network (CRN), spectrum sensing is an important prerequisite for improving the utilization of spectrum resources. In this paper, we propose a novel spectrum sensing method based on deep learning and cycle spectrum, which applies the advantage of the convolutional neural network (CNN) in an image to the spectrum sensing of an orthogonal frequency division multiplex (OFDM) signal. Firstly, we analyze the cyclic autocorrelation of an OFDM signal and the cyclic spectrum obtained by the time domain smoothing fast Fourier transformation (FFT) accumulation algorithm (FAM), and the cyclic spectrum is normalized to gray scale processing to form a cyclic autocorrelation gray scale image. Then, we learn the deep features of layer-by-layer extraction by the improved CNN classic LeNet-5 model. Finally, we input the test set to verify the trained CNN model. Simulation experiments show that this method can complete the spectrum sensing task by taking advantage of the cycle spectrum, which has better spectrum sensing performance for OFDM signals under a low signal-noise ratio (SNR) than traditional methods.

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Cross-term Suppression in Cyclic Spectrum Estimation Based on EMD

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Optimized Spectrum Sensing Algorithm for Cognitive Radio

Cited by 11 publications

References 27 publications

FPGA Implementation of Adaptive Absolute SCORE Algorithm for Cognitive Radio Spectrum Sensing with WTM and LFA

FPGA Implementation of Adaptive Absolute SCORE Algorithm for Cognitive Radio Spectrum Sensing with WTM and LFA

A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

Cross-term Suppression in Cyclic Spectrum Estimation Based on EMD

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