Real-Time In-Lab Test of Eigenvalue-Based Spectrum Sensing Using USRP RIO SDR Boards

Thameur, Hayfa Ben; Dayoub, Iyad

doi:10.1109/lcomm.2020.3037010

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…As shown in Table 3, the key difference between our developed work [23] and other works [26,27,29] is that we use highorder moments (Sk and Ku-3) to build the DMF method and the unsupervised ML algorithm (KMD) for the trained classifier, whereas other works use features based on second-order moments such as CF, EF, and EF of RMT to derive a fixed threshold. Except work [31], which employed supervised ML algorithms such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), decision trees (TREE), and K-Nearest Neighbors (KNN).…”

Section: Comparison Of the Developed Methods With Some Other Methodsmentioning

confidence: 99%

“…This would reduce the complexity of the proposed method and enhance the blind detection capability of the developed system [23]. In addition, deriving the threshold from the second-order moment is fixed, has a lot of computational complexity, and makes it hard to adapt to how the real channel of the PU signal changes over time [26,27,29]. Therefore, our proposed method has more benefits when it uses an unsupervised ML algorithm.…”

Section: Comparison Of the Developed Methods With Some Other Methodsmentioning

confidence: 99%

“…The researchers of these publications evaluated the practical sensing performance of an EFs realization based on USRP hardware for their theoretical performance and discussed their findings. The pa per [27,28]described the real-time realization of the SS method based on the covariance matrix of RMT employing USRP SDR hardware. In [29], a real-time adaptive SS method using EFs and CFs was realized by applying the USRP hardware platform and the GNU-Radio software throughout Rayleigh fading.…”

Section: Introductionmentioning

confidence: 99%

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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: Comparison Of the Developed Methods With Some Other Methodsmentioning

confidence: 99%

Section: Comparison Of the Developed Methods With Some Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SDR-based Intelligent Cooperative Spectrum Sensing for Cognitive Radio Systems

Radhi

Akkar

Abdullah

2023

ETJ

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“…Therefore, some recent works have tackled these issues. For example, the authors in [3], [6] successfully developed a testbed based on USRP-RIO and simulated a similar scenario to real-world MIMO communication. Currently, the number of communication devices has dramatically increased due to breakthrough innovations in technology and services such as 6G and massive Multiple-Input Multiple-Output (MIMO) [7].…”

Section: Introductionmentioning

confidence: 99%

Robust Automatic Modulation Classification in Low Signal to Noise Ratio

Lee

2023

IEEE Access

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In a non-cooperative communication environment, automatic modulation classification (AMC) is an essential technology for analyzing signals and classifying different kinds of signal modulation before they are demodulated. Deep learning (DL)-based AMC has been proposed as an efficient method of achieving high classification performance. However, most current DL-AMC methods have limited generalization capabilities under varying noise conditions, especially at low signal-to-noise ratios (SNRs). Therefore, these methods can not be directly applied to practical systems. In this paper, we propose a threshold autoencoder denoiser convolutional neural network (TADCNN), which consists of a threshold autoencoder denoiser (TAD) and a convolutional neural network (CNN). TADs reduce noise power and clean input signals, which are then passed on to CNN for classification. The TAD network generally consists of three components: the batch normalization layer, the autoencoder, and the threshold denoise. The threshold denoise component uses an auto-learning threshold sub-network to compute thresholds automatically. According to experiments, AMC with TAD improved classification accuracy by 70% at low SNR compared with a model without a denoiser. Additionally, our model achieves an average accuracy of 66.64% on the RML2016.10A dataset, which is 6% to 18% higher than the current AMC model.

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