Spectrum availability prediction based on RCS-GRU model

Cao, Kaitian; Jiang, Mengyan; Gao, Shenyao

doi:10.1016/j.phycom.2021.101479

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…It is used by hobbyist and academia and supports both wireless communications research and operational radio systems. The latest applications of GNU Radio environment include near-field measurements [33], wireless communication system development [34,35], spectrum sensing and availability [36,37], and machine learning [37,38].…”

Section: Receiver Sectionmentioning

confidence: 99%

SDR-Enabled Multichannel Real-Time Measurement System for In Situ EMF Exposure Evaluation

et al. 2022

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The spatial and temporal variability of the signals emitted by modern communication devices produced a paradigm shift in approaching the human exposure to electromagnetic fields (EMF). This inherent variability requires in situ, agile EMF measurement solutions capable of performing real-time isotropic measurements. The aim of this paper is to describe a new real-time, highly flexible multichannel EMF measurement system that consists of a sensor connected to state-of-the-art software-defined radio (SDR) equipment. In this paper an electric field sensor is proposed, but we also provide information on the extension of the probe to electric and magnetic fields. In the receiver section, the proposed solution is compared in terms of performances (sensitivity and accuracy), costs, and requirements, with standard solutions based on spectrum analyzers or a digital oscilloscope. Finally, the proposed solution was tested considering the signals emitted in various operating scenarios by a mobile device operating in the LTE-A and IEEE 802.11ax mobile communication standards. The results confirm the versatility and efficiency of the proposed solution for in situ EMF measurements of signals emitted by the new generation communication devices.

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Section: Receiver Sectionmentioning

confidence: 99%

SDR-Enabled Multichannel Real-Time Measurement System for In Situ EMF Exposure Evaluation

et al. 2022

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“…But LSTM is used to solve the time series problem, which is utilized in this work. There are few spectrum prediction works [28,29]. A deep neural network called Residual network and Channel and Spatial attention modules (RCS) with Gated Recurrent Unit network(RCS-GRU) is presented [28] for detecting occupancy correlation of different frequency channels by the PU.…”

Section: Introductionmentioning

confidence: 99%

“…There are few spectrum prediction works [28,29]. A deep neural network called Residual network and Channel and Spatial attention modules (RCS) with Gated Recurrent Unit network(RCS-GRU) is presented [28] for detecting occupancy correlation of different frequency channels by the PU. It also predicts the spectrum occupancy of the future next time slot.…”

Section: Introductionmentioning

confidence: 99%

LSTM Based Spectrum Prediction for Real-Time Spectrum Access for IoT Applications

Nandakumar¹,

Ponnusamy²,

Mishra³

2023

Intelligent Automation &Amp; Soft Computing

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In the Internet of Things (IoT) scenario, many devices will communicate in the presence of the cellular network; the chances of availability of spectrum will be very scary given the presence of large numbers of mobile users and large amounts of applications. Spectrum prediction is very encouraging for high traffic next-generation wireless networks, where devices/machines which are part of the Cognitive Radio Network (CRN) can predict the spectrum state prior to transmission to save their limited energy by avoiding unnecessarily sensing radio spectrum. Long short-term memory (LSTM) is employed to simultaneously predict the Radio Spectrum State (RSS) for two-time slots, thereby allowing the secondary node to use the prediction result to transmit its information to achieve lower waiting time hence, enhanced performance capacity. A framework of spectral transmission based on the LSTM prediction is formulated, named as positive prediction and sensing-based spectrum access. The proposed scheme provides an average maximum waiting time gain of 2.88 ms. The proposed scheme provides 0.096 bps more capacity than a conventional energy detector.

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“…In 2021, Hou et al (2021b) solved the problem of temperature prediction of switchgear equipment in substation by using long short-term memory (LSTM) network, and achieved good results, which opens the prelude of solving the problem of substation equipment temperature prediction with deep learning network. The gated recurrent unit (GRU) was proposed by Gharehbaghi et al (2022) and is an effective variant of LSTM ( Cao, Jiang & Gao, 2021 ; Yuan et al, 2022 ). In many cases, GRU and LSTM have the same excellent results, but GRU has fewer parameters, so it is relatively easy to train and the over fitting problem is lighter ( Cao, Jiang & Gao, 2021 ; Yuan et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

“…The gated recurrent unit (GRU) was proposed by Gharehbaghi et al (2022) and is an effective variant of LSTM ( Cao, Jiang & Gao, 2021 ; Yuan et al, 2022 ). In many cases, GRU and LSTM have the same excellent results, but GRU has fewer parameters, so it is relatively easy to train and the over fitting problem is lighter ( Cao, Jiang & Gao, 2021 ; Yuan et al, 2022 ). Therefore, GRU network is adopt to predict substation equipment temperature in this article.…”

Section: Introductionmentioning

confidence: 99%

Substation equipment temperature prediction based on multivariate information fusion and deep learning network

Sun

Liu

Wang

et al. 2022

PeerJ Computer Science

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Background Substation equipment temperature is difficult to achieve accurate prediction because of its typical seasonality, periodicity and instability, complex working environment and less available characteristic information. Methods To overcome these difficulties, a substation equipment temperature prediction method is proposed based on multivariate information fusion, convolutional neural network (CNN) and gated recurrent unite (GRU) in this article. Firstly, according to the correlation analysis including linear correlation mapping, autocorrelation function and partial autocorrelation function for substation equipment temperature data, the feature vectors from ambient, time and space are determined, that is the multivariate information fusion feature vector (denoted as MIFFV); secondly, the dimension of MIFFV is reduced by principal component analysis (PCA), extract some of the most important features and form the reduced feature vector (denoted as RFV); then, CNN is used for deep learning to extract the relationship between RFV and the high-dimensional space feature, and construct the high-dimensional feature vector of multivariate time series (denoted as HDFV); finally, the high-dimensional feature vector is used to train GRU deep learning network and predict the equipment temperature. Results A substation equipment in Taizhou City, Zhejiang Province is conducted by the method proposed in this article. Through the comparative experiment from the two aspects of features and methods, under the two prediction performance evaluation indexes of mean absolute percentage error (MAPE) and root mean square error (RSME), two main conclusions are drawn: (1) MIFFV from three aspects of ambient features, time features and space features have better prediction performance than the single feature vector and the combined feature vector of two aspects; (2) compared with other four related models under the same conditions, RFV is regarded as the input of the models, the proposed model has better prediction performance.

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Spectrum availability prediction based on RCS-GRU model

Cited by 6 publications

References 19 publications

SDR-Enabled Multichannel Real-Time Measurement System for In Situ EMF Exposure Evaluation

SDR-Enabled Multichannel Real-Time Measurement System for In Situ EMF Exposure Evaluation

LSTM Based Spectrum Prediction for Real-Time Spectrum Access for IoT Applications

Substation equipment temperature prediction based on multivariate information fusion and deep learning network

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