Implementation of SNR estimation based energy detection on USRP and GNU radio for cognitive radio networks

Talukdar, Jonti; Mehta, B.R.; Aggrawal, Kinjal; Kamani, Mansi

doi:10.1109/wispnet.2017.8299767

Cited by 8 publications

(3 citation statements)

References 15 publications

(20 reference statements)

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“…The PC is using a virtual machine (VMware® Workstation 16 Pro) to run GRC software version 3.8.1.0 based on the operating system (Ubuntu 20.04 LTS). The GRC is software that provides input/output blocks and enables the implementation of the DSP blocks and SDR algorithms of the proposed method, which is presented in experiment [39]. On the transmitting side, a single USRP N210-SDR transmitter is connected to a PC via Gigabit Ethernet.…”

Section: Methodsmentioning

confidence: 99%

SDR-based Intelligent Cooperative Spectrum Sensing for Cognitive Radio Systems

Radhi

Akkar

Abdullah

2023

ETJ

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 The real-time sensing performance of the new intelligent cooperative spectrum sensing based on the denoised mixed feature method paired with K-M edoids is verified.  Two RTL-SDR hardware receivers, a host laptop computer, and a USRP-SDR hardware transmitter are used.  The theoretical and practical sensing performance validity of the developed scheme is confirmed based on the SDR platform.In this paper, the software and hardware of a software-defined radio (SDR) platform are used to implement and verify the blind real-time sensing act of intelligent collaborative spectrum sensing based on a new theoretical formula for constructing denoised mixed features named M SKU3 and paired with an unsupervised machine learning K-M edoids algorithm. Two low-cost RTL-SDR dongle hardware receivers are used as two cooperative unlicensed secondary users to capture the radio frequency of a licensed primary user channel. A host personal computer is used as a fusion center to run GNU-Radio software signal processing blocks to implement the developed method, and a single Universal Software Radio Peripheral (USRP) N210 hardware transmitter based on FPGA is used to take up unoccupied desired radio frequency bandwidth. Two scenarios of signal-to-noise ratio levels have been adopted to verify and test the sensing performance of the developed system. The first one occurs when unlicensed secondary users have equal signal-to-noise ratio values. The second occurs when unlicensed secondary users have different signal-to-noise ratio values since each secondary user has their location. The experimental results of detecting action in terms of the probability of detection for the proposed method show that the theoretical and practical results are very close to each other.

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Section: Methodsmentioning

confidence: 99%

SDR-based Intelligent Cooperative Spectrum Sensing for Cognitive Radio Systems

Radhi

Akkar

Abdullah

2023

ETJ

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“…Some popular spectrum sensing methods include matched filter detection [14], cyclostationary feature detection [15], and energy detection [16]. Energy detection is the most popular approach because of its simplicity [17], [18]. The energy detection approach generally does not require any information about PU parameters though it still requires information related to the noise.…”

Section: Introductionmentioning

confidence: 99%

“…Several researches have evaluated the energy-detectionbased spectrum sensing approach simulated using a GNU Radio [16], [18], [20], [21]. In this paper, a spectrum sensing decision block that compares the energy of a signal and the threshold was built.…”

Section: Introductionmentioning

confidence: 99%

Performance of Energy Detection Spectrum Sensing for Cognitive Radio Using GNU Radio

Putra

Wibowo

Ariananda

et al. 2022

JNTETI

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The increasing number of wireless communication applications has led to spectrum scarcity problems. On the other hand, the current system in allocating the spectrum frequency is inefficient. To mitigate this issue, a cognitive radio (CR) system is proposed. CR is a smart radio that is able to sense the environment, locate the spectrum holes, and adapt its transmission parameter to exploit the existing spectrum holes. This underlines the importance of the spectrum sensing module to enable the operation of the CR system. The objective of the spectrum sensing module is to achieve the best utility from the available spectrum frequency. CR system is implemented in the unlicensed secondary users allowed to rent the spectrum currently not used by primary users (PU). In this paper, energy-detection-based spectrum sensing is implemented on the GNU Radio platform. We first implement the power spectral density (PSD) estimation method based on the periodogram by exploiting the Embedded Python block facility on the GNU Radio. Next, we implement the spectrum sensing decision module in the GNU Radio, which compares the PSD estimate of the PU signals corrupted by noise with a threshold. The PU signal is simulated as a bandpass random process occupying a particular frequency band. The spectrum sensing decision module is developed to allow the computation of the probability of detection (PD) and the probability of false alarm (PFA), which is performed by exploiting the Embedded Python block. One indicator to evaluate the performance of the spectrum sensing module is the receiver operating characteristic curve based on the computed PD and PFA on the GNU Radio. We evaluate the performance of the spectrum sensing for different SNRs and thresholds. The result shows that the energy-detection-based spectrum sensing is able to locate the existence of the PU when the signal-to-noise ratio (SNR) is sufficiently high.

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Detection of GSM Signal Using Energy Detection and Matched Filter-Based Techniques

Singh

Bhandari

2020

Algorithms for Intelligent Systems

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Implementation of SNR estimation based energy detection on USRP and GNU radio for cognitive radio networks

Cited by 8 publications

References 15 publications

SDR-based Intelligent Cooperative Spectrum Sensing for Cognitive Radio Systems

SDR-based Intelligent Cooperative Spectrum Sensing for Cognitive Radio Systems

Performance of Energy Detection Spectrum Sensing for Cognitive Radio Using GNU Radio

Detection of GSM Signal Using Energy Detection and Matched Filter-Based Techniques

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