Analysis study and SDR implementation of GoF‐based spectrum sensing for cognitive radio

Teguig, Djamal; Azzaz, Mohamed Salah; Sadoudi, Saïd

doi:10.1049/iet-com.2019.0711

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…Well understanding the performance trade-offs and limitations of disruptive technologies requires prototyping with realistic test-beds. Nevertheless, almost all previous studies [29][30][31] using SDR platforms have focused on implementing and evaluating the spectrum sensing methods and ignored the assessment of CSS in real wireless environment.…”

Section: Introductionmentioning

confidence: 99%

Experimental SDR implementation of cooperative spectrum sensing in cognitive radio networks

Bouzegag¹,

Teguig²,

Maali³

2022

Phys. Scr.

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In this paper, a Software Defined Radio (SDR) implementation of Cooperative Spectrum Sensing (CSS) is investigated. More precisely, various local spectrum sensing algorithms have been implemented and validated on USRP B210 using the Qt framework. The realized test bed is based on a client-server architecture. In the client interface, different spectrum sensing techniques have been implemented and analyzed. The fusion center interface implements all hard decision combination schemes, namely OR, AND, and the MAJORITY rule. Finally, several realistic scenarios have been considered to validate our SDR implementation. Moreover, these implementations allowed us to assess the efficiency of different hard-decision combination schemes for cooperative detection in the context of cognitive radio. Besides, the performance improvement is analytically evaluated and corroborated by the experimental results. These results regarding real scenarios confirm that Goodness of Fit-based (GoF) sensing methods outperform conventional methods. Furthermore, theoretical and simulation results from CSS have been validated experimentally that OR-rule gives clearly better results than other hard decisions combinations schemes.

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Section: Introductionmentioning

confidence: 99%

Experimental SDR implementation of cooperative spectrum sensing in cognitive radio networks

Bouzegag¹,

Teguig²,

Maali³

2022

Phys. Scr.

Self Cite

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“…In [22], SDR and USRP devices were used to implement a communication system to detect interferences performing significantly better than the ED in the detection of weak interference, offering the capability of detecting 8 to 10 dB lower values of interference. In [23], experimental results of the USRP hardware implementation for the detection of 'gaps' in the spectrum were presented with the advantage of requiring fewer samples than the ED and obtaining the same performance. In [24], a new SDR platform architecture was proposed that implements different modulation techniques, and this architecture showed favorable gains with an advantage over traditional techniques, such as spatial multiplexing multipleinput multiple-output (MIMO) systems.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology

Molina-Tenorio

Prieto‐Guerrero

Aguilar-Gonzalez

2021

Sensors

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In this work, a novel multiband spectrum sensing technique is implemented in the context of cognitive radios. This technique is based on multiresolution analysis (wavelets), machine learning, and the Higuchi fractal dimension. The theoretical contribution was developed before by the authors; however, it has never been tested in a real-time scenario. Hence, in this work, it is proposed to link several affordable software-defined radios to sense a wide band of the radioelectric spectrum using this technique. Furthermore, in this real-time implementation, the following are proposed: (i) a module for the elimination of impulsive noise, with which the appearance of sudden changes in the signal is reduced through the detail coefficients of the multiresolution analysis, and (ii) the management of different devices through an application that updates the information of each secondary user every 100 ms. The performance of these linked devices was evaluated with encouraging results: 95% probability of success for signal-to-noise ratio (SNR) values greater than 0 dB and just five samples (mean) in error of the edge detection (start and end) for a primary user transmission.

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SDR implementation of wideband spectrum sensing using machine learning

Sabrina,

Camel,

Djamal

et al. 2024

Int J Communication

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SummaryNew cognitive radio (CR) systems require high throughput and bandwidth. Hence, CR users need to detect wide frequency bands of the radio spectrum to exploit unused frequency channels. This paper proposes a new wideband spectrum sensing (WBSS) detection approach based on machine learning (ML) for scanning subchannels. The originality of the proposed approach is to detect spectrum opportunities using a narrowband spectrum sensing (NBSS) method‐based support vector machine (SVM) classification and two features: energy and goodness of fit (GoF). The simulation results show that the proposed WBSS approach‐based ML presents a higher probability of detection than the WBSS approach‐based conventional detectors, even at low signal‐to‐noise ratio (SNR). Finally, the software defined radio (SDR) implementation validates the proposed WBSS approach for real detection scenarios.

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Analysis study and SDR implementation of GoF‐based spectrum sensing for cognitive radio

Cited by 7 publications

References 31 publications

Experimental SDR implementation of cooperative spectrum sensing in cognitive radio networks

Experimental SDR implementation of cooperative spectrum sensing in cognitive radio networks

Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology

SDR implementation of wideband spectrum sensing using machine learning

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