Sensing OFDM Signal: A Deep Learning Approach

Cheng, Qingqing; Shi, Zhenguo; Nguyen, Diep N.; Dutkiewicz, Eryk

doi:10.1109/tcomm.2019.2940013

Cited by 41 publications

(17 citation statements)

References 27 publications

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“…Furthermore, few works have also applied the DL approach for spectrum sensing. For instance, a convolutional neural network (CNN) based spectrum sensing was proposed in [39]- [41], CNN based cooperative sensing in [42] while stacked auto-encoder based spectrum sensing of OFDM signal was proposed in [43]. The above mentioned ML/DL frameworks have shallow/deep multilayer perceptron network.…”

Section: A Current State Of the Art And Motivationmentioning

confidence: 99%

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

2020

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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.

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Section: A Current State Of the Art And Motivationmentioning

confidence: 99%

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

2020

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“…To overcome the efforts required in feature engineering for ML approaches, few works have also applied the DL approach to spectrum sensing. For instance, a convolutional neural network (CNN) based spectrum sensing was proposed in [21]- [23], CNN based cooperative sensing was implemented in [24], while stacked auto-encoder based spectrum sensing of OFDM signals was proposed in [25]. Moreover, long short term memory (LSTM) based spectrum sensing schemes were proposed in [26], [27].…”

Section: A State-of-the-art and Motivationmentioning

confidence: 99%

PU-DetNet: Deep Unfolding Aided Smart Sensing Framework for Cognitive Radio

Soni

Patel

Shah³

et al. 2022

IEEE Access

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Spectrum sensing in cognitive radio (CR) paradigm can be broadly categorized as analyticalbased and data-driven approaches. The former is sensitive to model inaccuracies in evolving network environment, while the latter (machine learning (ML)/deep learning (DL) based approach) suffers from high computational cost. For devices with low computational abilities, such approaches could be rendered less useful. In this context, we propose a deep unfolding architecture namely the Primary User-Detection Network (PU-DetNet) that harvests the strength of both: analytical and data-driven approaches. In particular, a technique is described that reduces computation in terms of inference time and the number of floating point operations (FLOPs). It involves binding the loss function such that each layer of the proposed architecture possesses its own loss function whose aggregate is optimized during training. Compared to the state-of-the-art, experimental results demonstrate that at SNR= -10 dB, the probability of detection is significantly improved as compared to the long short term memory (LSTM) scheme (between 39% and 56%), convolutional neural network (CNN) scheme (between 45% and 84%), and artificial neural network (ANN) scheme (between 53% and 128%) over empirical, 5G new radio, DeepSig, satellite communications, and radar datasets. The accuracy of proposed scheme also outperforms other existing schemes in terms of the F1-score. Additionally, inference time reduces by 91.69%, 90.90%, and 93.15%, while FLOPs reduces by 62.50%, 56.25%, 64.70% w.r.t. LSTM, CNN and ANN schemes, respectively. Moreover, the proposed scheme also shows improvement in throughput by 56.39%, 51.23%, and 69.52% as compared to LSTM, CNN and ANN schemes respectively, at SNR = -6 dB.

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“…Furthermore, a few works have also applied the DL approach to spectrum sensing. For instance, a convolutional neural network (CNN) based spectrum sensing was proposed in [24]- [26], CNN based cooperative sensing was implemented in [27], while stacked auto-encoder based spectrum sensing of OFDM signal was proposed in [28]. Moreover, long short term memory (LSTM) based spectrum sensing schemes were studied in [29]- [30] while, various other DL architectures for spectrum sensing schemes were carried out in [31]- [34].…”

Section: A Background and Current State-of-the-artmentioning

confidence: 99%

PU-DetNet: An Efficient Deep Unfolding aided Smart Sensing Framework for Cognitive Radio

Shah¹,

Patel²,

Soni³

et al. 2022

Preprint

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Spectrum sensing in cognitive radio paradigm can be categorized as model/analytical and data-based approaches. However, the former is sensitive to model inaccuracies in evolving network environments, while the latter (machine learning/deep learning approach) suffers from high computational complexity and black box-like behaviour. For devices with low computational abilities, such approaches could be rendered less useful. In this context, we propose a deep unfolding architecture namely Primary User-Detection Network (PU-DetNet) that harvests the strength of both: model and data-based approach. In particular, a technique is described that reduces computational complexity. It involves binding loss function such that each layer of proposed architecture possesses its own loss function whose aggregate is optimized during training. Compared to state-of-the-art, experimental results demonstrate that probability of detection is significantly improved as compared to long short term memory (LSTM) scheme (between 39% and 56%), convolutional neural network (CNN) scheme (between 45% and 84%), and artificial neural network (ANN) scheme (between 53% and 128%) over empirical, 5G-simulated, DeepSig, satellite-communications, and radar dataset. Additionally, computational complexity reduces by 91.69% and 93.15% w.r.t. LSTM and ANN schemes respectively. Moreover, the proposed scheme also shows improvement in throughput by 87% and 130% w.r.t. LSTM and ANN schemes, respectively.

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Sensing OFDM Signal: A Deep Learning Approach

Cited by 41 publications

References 27 publications

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

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

PU-DetNet: Deep Unfolding Aided Smart Sensing Framework for Cognitive Radio

PU-DetNet: An Efficient Deep Unfolding aided Smart Sensing Framework for Cognitive Radio

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