Spectrum sensing for cognitive radio based on convolution neural network

Han, Dong Seog; Sobabe, Gounou Charles; Zhang, Chenjie; Bai, Xuemei; Wang, Zhijun; Liu, Shuai; Guo, Bin

doi:10.1109/cisp-bmei.2017.8302117

Cited by 56 publications

(38 citation statements)

References 12 publications

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“…Detection of RF signals under low SNR regime poses a great problem towards successful utilization of RF spectrum by opportunistic users [19]. In [16], authors attempt to solve the pressing issue (of detection under low SNR) by using CNN algorithm. The data collection for the proposed experimentation is performed by using Cyclostationary feature detector (An algorithm that converts the received signal into its signatures) and energy detection (An algorithm that measures the energy of the received signals).…”

Section: Related Workmentioning

confidence: 99%

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Opportunistic use of Spectral Holes in Karachi using Convolutional Neural Networks

Shaikh¹,

Naqvi²,

Ali³

et al. 2020

IJACSA

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Wireless services appearing in the next generation wireless standard i.e. 6G include Internet of Everything (IoE), Holographic communications, smart transportation and smart cities require exponential rise in the bandwidth in addition to other requirements. The current static spectrum allocation policy does not allow any new entrant to exploit already grid-locked Radio Frequency (RF) spectrum. Hence, quest for larger bandwidth can be fulfilled through other technologies. These include exploiting sub-Terahertz band, Visible Light Communication and Cognitive Radio scheme or exploiting of RF bands in opportunistic fashion. Cognitive Radio is one of those engines to exploit the RF spectrum in secondary style. Cognitive Radio can use artificial intelligence driven algorithms to complete the task. Several intelligent algorithms can be used for better forecasting of spectral holes. Convolutional Neural Network (CNN) is a Deep Learning algorithm that can be used to predict the presence of a spectral hole that can be opportunistically exploited for efficient utilization of RF spectrum in secondary fashion. This paper investigates the performance of CNN for metropolitan Karachi city of Pakistan so that the users can be provided with uninterrupted access to the network even under busy hours. Dataset for the proposed setup is collected for 1805 MHz frequency band through NI 2901 Universal Software Radio Peripheral (USRP) devices. The root mean square error (RMSE) for the predicted results using CNN appears to be 81.02 at epoch of 200 and mini-batch loss of 3281.8. Based on the predicted results, it was concluded that CNN can be useful for investigating the possible opportunistic usage of RF spectrum; however, further investigation is required with different datasets.

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Section: Related Workmentioning

confidence: 99%

“…The operation of convolution strengthens and reduces interference with the original signal features and have better tolerance to noise. The training parameters in CNN are less than in a fully connected network [16]. Two factors including Root Mean Square Error (RMSE) and loss function have been used to interpret the results.…”

Section: Introductionmentioning

confidence: 99%

Opportunistic use of Spectral Holes in Karachi using Convolutional Neural Networks

Shaikh¹,

Naqvi²,

Ali³

et al. 2020

IJACSA

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“…Various CNN models have been widely exploited in the field of cognitive radio [14], which uses CNN-based spectral sensing, where the input data consist of a two-dimensional matrix composed of the cyclo-stationary feature and energy feature. In the proposed scheme, the received time-series signal is converted into an RP image that is used as input data for the subsequent CNN.…”

Section: Cnn Structurementioning

confidence: 99%

“…The authors of [12] discussed a cooperative spectrum sensing (CSS) based on the CNN scheme for CRNs, which is constructed using a sensing matrix that takes into account the spatial and spectral correlations of the channels [13]. In [14], the spectrum sensing of a single SU based on the CNN was taken into account, where the feature of the presence of the PU signal is extracted and adopted to the input data, which is fed to the CNN. [13], [15] used the covariance matrix (CM) of signal samples as the input to the CNN.…”

Section: Introductionmentioning

confidence: 99%

Deep RP-CNN for Burst Signal Detection in Cognitive Radios

Seo

Nam

2020

IEEE Access

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This paper proposes a convolutional neural network (CNN)-based signal detection scheme using image encoding techniques for burst signals in wireless networks. The conventional signal detection approach based on energy measurement performs poorly when detecting burst signals owing to the short signal length and relatively long sensing duration. To detect the presence of a burst signal, the proposed scheme encodes the received time-series signal into an image that is further fed to a CNN model. For image encoding techniques, recurrence plot algorithms are adopted in the proposed scheme with a CNN. In particular, the proposed scheme achieves the correct detection probability of 99% even in the presence of a short burst signal at SNR= −10dB.

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“…Using machine learning techniques in CSS has become increasingly popular and CSS based on support vector machine was introduced in [21]. Spectrum sensing by a single SU using a convolutional neural network (CNN) was proposed in [22], and the same for multiple SUs in [23]. A recent study [24] shows that machine learning techniques can outperform traditional signal detection methods for spectrum sensing.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Online Learning for CSS in Frugal IoT Network

Nayak

Raj

Kalyani

2020

IEEE Trans. Cogn. Commun. Netw.

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We present a novel method for centralized collaborative spectrum sensing for IoT network leveraging cognitive radio network. Based on an online learning framework, we propose an algorithm to efficiently combine the individual sensing results based on the past performance of each detector. Additionally, we show how to utilize the learned normalized weights as a proxy metric of detection accuracy and selectively enable the sensing at detectors. Our results show improved performance in terms of inter-user collision and misdetection. Further, by selectively enabling some of the devices in the network, we propose a strategy to extend the field life of devices without compromising on detection accuracy.

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Spectrum sensing for cognitive radio based on convolution neural network

Cited by 56 publications

References 12 publications

Opportunistic use of Spectral Holes in Karachi using Convolutional Neural Networks

Opportunistic use of Spectral Holes in Karachi using Convolutional Neural Networks

Deep RP-CNN for Burst Signal Detection in Cognitive Radios

Leveraging Online Learning for CSS in Frugal IoT Network

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