Spectrum Occupancy Prediction for Realistic Traffic Scenarios: Time Series versus Learning-Based Models

Agarwal, Anirudh; Sengar, Aditya Singh; Gangopadhyay, R.

doi:10.1007/s41650-018-0013-6

Cited by 14 publications

(4 citation statements)

References 13 publications

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“…In paper [9], a novel real time detection method based spectrum sensing technique using a logistic regression classifier, which is implemented using universal software radio peripheral (USRP) and GNU-radio, has been proposed and it can achieve a high success detection ratio of over 95% for the 2.4 GHz ISM band. In paper [10], prediction through ML-based recurrent neural network proves to perform reasonably well, thereby it provides an accurate future spectrum occupancy information for DSA.…”

Section: Introductionmentioning

confidence: 95%

“…There are many proposals and methods considering the channel spectrum status and COR prediction research [7][8][9][10]. A hybrid technology that combines both auto-regressive integrated moving average (ARIMA) and artificial neural networks (ANNs) is taking advantages of the unique strength of ARIMA and ANN models in linear and nonlinear modeling has been researched in [7].…”

Section: Introductionmentioning

confidence: 99%

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Channel Status Prediction using Auto-regressive and Auto-regressive Integrated Predictors over WLAN Channel

Hou¹,

Naoya²,

Denno³

2020

Int J Comput Softw Eng

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Recently, due to the increase of huge number of wireless devices such as smartphones or sensors, mobile wireless traffic is dramatically expanding each year. Cognitive radio (CR) system has been attracted attention to improve frequency usage efficiency. CR system is a technology that enables to select multiple radio systems, grasps the congestion status of communication and selects the optimum radio system. Till now, there are many researches considering the prediction of channel occupancy ratio (COR: the ration between busy duration length to resolution period T). If the start and end points of busy/idle duration from the sensing channel spectrum can be correctly predicted, it will largely benefit the wireless system design and spectrum efficiency (SE) improvement. In this paper, we will consider such research based on auto-regressive (AR) and auto-regressive integrated (ARI) models using traffic data captured from the wireless channel near a railway station. The major idea is that the busy/idle duration length can be calculated from COR value when the resolution period T is short. The results confirm that our proposal can improve the prediction accuracy.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Channel Status Prediction using Auto-regressive and Auto-regressive Integrated Predictors over WLAN Channel

Hou¹,

Naoya²,

Denno³

2020

Int J Comput Softw Eng

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“…Furthermore, ML and DL are used for spectrum perceptions, i.e., prediction of the occupancy of the PU channels [ 89 , 103 , 104 ]. For example, a geo-frequency-temporal map on the PU activities can be constructed using learning techniques.…”

Section: Learning Techniques For Spectrum Sensingmentioning

confidence: 99%

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

Nasser

Hassan

Chaaya

et al. 2021

Sensors

137

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Spectrum Sensing (SS) plays an essential role in Cognitive Radio (CR) networks to diagnose the availability of frequency resources. In this paper, we aim to provide an in-depth survey on the most recent advances in SS for CR. We start by explaining the Half-Duplex and Full-Duplex paradigms, while focusing on the operating modes in the Full-Duplex. A thorough discussion of Full-Duplex operation modes from collision and throughput points of view is presented. Then, we discuss the use of learning techniques in enhancing the SS performance considering both local and cooperative sensing scenarios. In addition, recent SS applications for CR-based Internet of Things and Wireless Sensors Networks are presented. Furthermore, we survey the latest achievements in Spectrum Sensing as a Service, where the Internet of Things or the Wireless Sensor Networks may play an essential role in providing the CR network with the SS data. We also discuss the utilisation of CR for the 5th Generation and Beyond and its possible role in frequency allocation. With the advancement of telecommunication technologies, additional features should be ensured by SS such as the ability to explore different available channels and free space for transmission. As such, we highlight important future research axes and challenging points in SS for CR based on the current and emerging techniques in wireless communications.

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“…In this regard, ref. [4] applied Autoregressive Integrated Moving Average (ARIMA) models, Lagrangian Support Vector Machine, and an Elman network (simplified models of Recurrent Neural Networks (RNNs)) are used to predict spectrum occupancy in a TV and cellular bands. The results show that the RNN technique outperforms the other models in prediction accuracy for cellular networks, as it better captures the non-stationarity and several irregularities in the data traffic.…”

Section: Introductionmentioning

confidence: 99%

K-Means Clustering Assisted Spectrum Utilization Prediction with Deep Learning Models

Shawel

Bantigegn²,

Debella

et al. 2022

The 8th International Conference on Time Series and Forecasting

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Spectrum Occupancy Prediction for Realistic Traffic Scenarios: Time Series versus Learning-Based Models

Cited by 14 publications

References 13 publications

Channel Status Prediction using Auto-regressive and Auto-regressive Integrated Predictors over WLAN Channel

Channel Status Prediction using Auto-regressive and Auto-regressive Integrated Predictors over WLAN Channel

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

K-Means Clustering Assisted Spectrum Utilization Prediction with Deep Learning Models

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