Radio access technology classification for cognitive radio networks

Baban, Shaswar; Denkoviski, Daniel; Holland, Oliver; Gavrilovska, Liljana; Aghvami, Hamid

doi:10.1109/pimrc.2013.6666608

Cited by 7 publications

(10 citation statements)

References 6 publications

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“…10 the accuracy of three-class RAT identification in the same frequency band using the neural network algorithm of [23] converges around 33%. However, the accuracy of three-class RAT identification in separate frequency bands in [23] exceeds 90%. This is because the radio features of identification target RATs in [23] are not so much similar to each other as the features adopted in our research.…”

Section: Relearning Classificationmentioning

confidence: 91%

“…The accuracy of three-class RAT identification converges around 82% even if it relearns the training data, whereas in Fig. 10 the accuracy of three-class RAT identification in the same frequency band using the neural network algorithm of [23] converges around 33%. However, the accuracy of three-class RAT identification in separate frequency bands in [23] exceeds 90%.…”

Section: Relearning Classificationmentioning

confidence: 96%

“…To evaluate the effect of relearning on identification accuracy, we calculated the accuracy of the relearned RAT classifier and compared it with the results marked Proposal in Fig. 9 and the results of [23] for the accuracy of three-class RAT that exist in separate frequency bands. In [23], the RAT classifier was made by a neural network algorithm, and thus we generate the same one to compare with Proposal.…”

Section: Relearning Classificationmentioning

confidence: 99%

“…9 and the results of [23] for the accuracy of three-class RAT that exist in separate frequency bands. In [23], the RAT classifier was made by a neural network algorithm, and thus we generate the same one to compare with Proposal. The parameters of the neural network used in the present experiment and [23] are listed in Table 2, and the comparison results are shown in Fig.…”

Section: Relearning Classificationmentioning

confidence: 99%

See 3 more Smart Citations

Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio

Asakura

Ichikawa

Kawakita

2019

SICE Journal of Control, Measurement, and System Integration

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The identification of the radio access technology (RAT) of the primary user by secondary users is important to avoid interference for spectrum-sharing techniques using cognitive radio (CR). RATs have become more diversified with the introduction of various services that use wireless communication. Therefore, it is desirable that a RAT identification system, which can easily cope with the diversification of RATs, is applied to CR. The purpose of this study is to use online learning to identify multiple RATs in the same frequency band. To improve the accuracy of identifying RATs with similar features, we evaluate a normalization of radio feature method proposed in our previous research for the features extracted from the signal's spectrogram. We evaluate the RAT classifier created using the proposed method by calculating the curve of receiver operating characteristics (ROC) and the identification accuracy. The results for the ROC curve show that the proposed method is effective for several supervised learning methods. Moreover, the results for the identification accuracy show that the proposed method improves the identification performance compared to the identification accuracy of conventional methods.

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Section: Relearning Classificationmentioning

confidence: 91%

Section: Relearning Classificationmentioning

confidence: 96%

Section: Relearning Classificationmentioning

confidence: 99%

Section: Relearning Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio

Asakura

Ichikawa

Kawakita

2019

SICE Journal of Control, Measurement, and System Integration

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“…For instance, ANN was implemented for channel sensing [17][18][19] and spectrum prediction [17,20], etc. To enhance ANN classification accuracy, ANN is usually combined with the extracted features from the received signal, which allows the engine to have the capability to identify the modulation scheme at low SNR levels.…”

Section: Related Workmentioning

confidence: 99%

Very-low-SNR cognitive receiver based on wavelet preprocessed signal patterns and neural network

Alzaq

Ustundag

2017

J Wireless Com Network

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A pattern-based cognitive communication system (PBCCS) that optimizes non-periodic RF waveforms for security applications is proposed. PBCCS is a cross-layer approach that merges the channel encoding and modulation. The transmitter encodes sequences of bits into continuous signal patterns by selecting the proper symbol glossaries. The cognitive receiver preprocesses the received signal by extracting a limited set of wavelet features. The extracted features are fed into an artificial neural network (ANN) to recover the digital data carried by the distorted symbol. The PBCCS system offers a flexible management for robustness against a high noise level and increases the spectral efficiency. In this study, the spectral efficiency and robustness of a PBCCS scheme for an additive white Gaussian noise (AWGN) channel is investigated. The results show that at an SNR of −5 dB, a 3-bit glossary achieves a bit error rate (BER) of 10 −5 . Also, the link spectral efficiency (LSE) of the proposed system is 2.61 bps/Hz.

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Cognitive Radio Engine Design for IoT Using Monarch Butterfly Optimization and Fuzzy Decision Making

Goudos

2020

Internet of Things

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Radio access technology classification for cognitive radio networks

Cited by 7 publications

References 6 publications

Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio

Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio

Very-low-SNR cognitive receiver based on wavelet preprocessed signal patterns and neural network

Cognitive Radio Engine Design for IoT Using Monarch Butterfly Optimization and Fuzzy Decision Making

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