Deep CM-CNN for Spectrum Sensing in Cognitive Radio

Liu, Chang; Wang, Jie; Liu, Xuemeng; Liang, Ying-Chang

doi:10.1109/jsac.2019.2933892

Cited by 220 publications

(114 citation statements)

References 38 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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“…Rajendran et al [13] achieved the automatic modulation classification based on the LSTM model, which learns from the time domain amplitude and phase information of the modulation schemes present in the training data for a distributed wireless spectrum sensing network. In [14], Liu et al used a deep neural network (DNN) to explore the data-driven test statistic intelligently and proposed a covariance matrix-aware CNN-based spectrum sensing algorithm to improve the detection performance further. In [15], the machine learning algorithms were demonstrated that appreciably outperform classical signal detection methods in the 3.5-GHz band.…”

Section: A Supervised Learning-based Mac Designmentioning

confidence: 99%

“…Therefore, the over-estimation deteriorates the system throughput of the SL-MAC protocol. On the other hand, when under-estimation occurs, we can infer from (14) that the impact of inference errors on system throughput performance of SL-MAC is not decisive, i.e., the total throughput may decrease or remain the same. However, since only a portion of STAs suffering collisions can be scheduled to transmit data within the TXOP, the remaining STAs not being scheduled will double the contention window size following the traditional CSMA/CA scheme.…”

Section: ) Case 1: Over-estimationmentioning

confidence: 99%

Improving Medium Access Efficiency With Intelligent Spectrum Learning

et al. 2020

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Through machine learning, this paper changes the fundamental assumption of the traditional medium access control (MAC) layer design. It obtains the capability of retrieving the information even the packets collide by training a deep neural network offline with the historical radio frequency (RF) traces and inferring the STAs involved collisions online in near-real-time. Specifically, we propose a MAC protocol based on intelligent spectrum learning for the future wireless local area networks (WLANs), called SL-MAC. In the proposed MAC, an access point (AP) is installed with a pre-trained convolutional neural network (CNN) model to identify the stations (STAs) involved in the collisions. In contrast to the conventional contention-based random medium access methods, e.g., IEEE 802.11 distributed coordination function (DCF), the proposed SL-MAC protocol seeks to schedule data transmissions from the STAs suffering from the collisions. To achieve this goal, we develop a two-step offline training algorithm enabling the AP to sense the spectrum with the aid of the CNN. In particular, on receiving the overlapped signal(s), the AP firstly predicts the number of STAs involving collisions and then further identifies the STAs' ID. Furthermore, we analyze the upper bound of throughput gain brought by the CNN predictor and investigate the impact of the inference error on the achieved throughput. Extensive simulations show the superiority of the proposed SL-MAC and allow us to gain insights on the trade-off between performance gain and the inference accuracy. INDEX TERMS Medium access control (MAC), spectrum sensing, deep learning, convolutional neural network (CNN).

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“…In classic spectrum sensing algorithms, typically model based approach is applied to sense the wireless environment i.e. energy and other methods, however, to devise better results the authors in [20] recommend a data driven approach to solve the problem of spectrum sensing through Deep convolutional network based spectrum monitoring approach. The proposed algorithm uses data to train itself.…”

Section: Related Workmentioning

confidence: 99%

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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Deep CM-CNN for Spectrum Sensing in Cognitive Radio

Cited by 220 publications

References 38 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

Improving Medium Access Efficiency With Intelligent Spectrum Learning

Opportunistic use of Spectral Holes in Karachi using Convolutional Neural Networks

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