Artificial neural network based hybrid spectrum sensing scheme for cognitive radio

Vyas, Maunil R.; Patel, Dhaval; López-Benítez, Miguel

doi:10.1109/pimrc.2017.8292449

Cited by 61 publications

(53 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To have a fair comparison with the results of [36], the P f close to three decimal places was chosen from Table 5. The results show that although the performance of the proposed LSTM-SS scheme at high SNRs is almost the same, it significantly outperforms the ANN-based sensing [36], improved energy detection based sensing [56] and classical energy detection at low SNRs. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The datasets used are FM broadcasting and UHF television. To have a fair comparison of the proposed LSTM based sensing with other schemes like CNN [40], ANN [36] and CED, the data was kept same for all sensing schemes, and the model was designed and trained accordingly. Due to the single column data structure, we adopted the 1D-CNN model while keeping the CNN architecture similar as proposed in [40].…”

Section: Resultsmentioning

confidence: 99%

“…Once the model was trained, it was tested on the test dataset. Similarly, for ANN-based sensing, we constructed and utilized the model as mentioned in [36]. We can infer from the plot that the proposed LSTM-SS scheme outperforms other sensing schemes at a very low SNR of −14 dB.…”

Section: Resultsmentioning

confidence: 99%

“…However, LSTM's are designed in a way such that the information persists due to the looping FIGURE 11. Comparison of detection probability for the considered spectrum sensing schemes: proposed LSTM-SS, ANN-based sensing [36], Improved Energy Detection (IED) [56], and Classical Energy Detection (CED) for N=100. structure or (chain-like structure when repeating elements are unrolled), as opposed to ANN and CNN architectures where the looping is absent.…”

Section: Resultsmentioning

confidence: 99%

“…For instance, Artificial neural network (ANN) based spectrum sensing was carried out in [35]. In [36], a novel ANN-based hybrid sensing scheme was proposed which used energy values and the Zhang statistic [37] as the training features. Recently, the sensing of the OFDM signal at a low SNR regime using a naive Bayes classifier was proposed in [38].…”

Section: A Current State Of the Art And Motivationmentioning

confidence: 99%

See 4 more Smart Citations

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

2020

View full text Add to dashboard Cite

The cognitive radio (CR) network consists of primary users (PUs) and secondary users (SUs). The SUs in the CR network senses the spectrum band to opportunistically access the white space. Exploiting the white spaces helps to improve the spectrum efficiency. Owing to the excellent learning ability of machine learning/deep learning framework, many works in the recent past have applied shallow/deep multi-layer perceptron approach for spectrum sensing. However, the multi-layer perceptron networks are not well suited for time-series data due to the absence of memory elements. On the other hand, long short-term memory (LSTM) network, an improved version of Recurrent neural network is well suited for time-series data. In this paper, we propose an LSTM based spectrum sensing (LSTM-SS), which learns the implicit features from the spectrum data, for instance, the temporal correlation (i.e., the correlation between the present and past timestamp).Moreover, the CR systems also exploits the PU activity statistics to improve the CR performance. In this context, we compute the PU activity statistics like on and off period duration, duty cycle and propose the PU activity statistics based spectrum sensing (PAS-SS) to enhance the sensing performance. The proposed sensing schemes are validated on the spectrum data of various radio technologies acquired using an experimental test-bed setup. The proposed LSTM-SS scheme is compared with the state of the art spectrum sensing techniques. Experimental results indicate that the proposed schemes has improved detection performance and classification accuracy at low signal to noise ratio regimes. We notice that the improvement achieved is at the cost of longer training time and a nominal increase in execution time.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: A Current State Of the Art And Motivationmentioning

confidence: 99%

See 3 more Smart Citations

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

2020

View full text Add to dashboard Cite

show abstract

Machine learning for cooperative spectrum sensing and sharing: A survey

Janu

Singh

Kumar

2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

With the rapid development of next-generation wireless communication technologies and the increasing demand of spectrum resources, it becomes necessary to introduce learning and reasoning capabilities in cognitive radio networks (CRN). In particular, our focus is on two fundamental applications in CRNs, namely spectrum sensing (SS) and spectrum sharing. The application of machine learning (ML) techniques has added new aspects to SS and spectrum sharing. This paper offers a survey on various ML-based algorithms in the cooperative spectrum sensing (CSS) and dynamic spectrum sharing (DSS) domain, with its emphasis on types of features extracted from primary user signal, types of ML algorithm, and performance metrics utilized for evaluation of ML algorithms. Starting with the basic principles and challenges of SS, this paper also justifies the applicability of supervised, unsupervised, and reinforcement ML algorithms in the CSS domain. The application of ML algorithms, to solve the DSS problem has also been reviewed. Finally, the survey paper is concluded with some suggested open research challenges and future directions for ML application in next-generation communication technologies.

show abstract