Learning based primary user activity prediction in cognitive radio networks for efficient dynamic spectrum access

Agarwal, Anirudh; Dubey, Shivangi; Khan, Mohd Adnan; Gangopadhyay, R.; Debnath, Soumitra

doi:10.1109/spcom.2016.7746632

Cited by 61 publications

(34 citation statements)

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“…Although several existing works assume the prior knowledge about the spectrum occupancy information such as the state of a channel (idle/busy) and the received power, such a prior knowledge is difficult to acquire in practice and these parameters need to be estimated [97]. For modeling the PU activity, existing works mostly use ON/OFF models such as two state Markov model, Bernoulli and exponential models, and recently, the concept of using learning based PU modeling is getting attention in the literature [41,155]. In practice, parameters related to the PU traffic/channel can be estimated by employing the following approaches: (i) statistical analysis of sensing measurements obtained from spectrum occupancy measurement campaigns [30], (ii) spectrum prediction models like hidden Markov model and Bayesian interference model [156], and (ii) Radio Environment Map (REM) which can be created either based on sensing information obtained from the sensor nodes or database information obtained from regulators/operators or both [32,157].…”

Section: A Primary Traffic Modelmentioning

confidence: 99%

Dynamic Spectrum Sharing in 5G Wireless Networks With Full-Duplex Technology: Recent Advances and Research Challenges

Sharma

Bogale

et al. 2018

IEEE Commun. Surv. Tutorials

213

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Full-Duplex (FD) wireless technology enables a radio to transmit and receive on the same frequency band at the same time, and it is considered to be one of the candidate technologies for the fifth generation (5G) of wireless communications due to its advantages including potential doubling of the capacity, reduced end-to-end and feedback delays, improved network secrecy and efficiency, and starting with a detailed overview of FD-enabled DSS, we provide a comprehensive survey of recent advances in this domain. We then highlight several potential techniques for enabling FD operation in DSS wireless systems. Subsequently, we propose a novel communication framework to enable CST in DSS systems by employing a power control-based SI mitigation scheme and carry out the throughput performance analysis of this proposed framework. Finally, we discuss some open research issues and future directions with the objective of stimulating future research efforts in the emerging FD-enabled DSS wireless systems.

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Section: A Primary Traffic Modelmentioning

confidence: 99%

Dynamic Spectrum Sharing in 5G Wireless Networks With Full-Duplex Technology: Recent Advances and Research Challenges

Sharma

Bogale

et al. 2018

IEEE Commun. Surv. Tutorials

213

View full text Add to dashboard Cite

show abstract

“…Spectrum prediction is achieved by using neural network multilayer perceptron (MLP) which improves the spectrum utilization and also reduces the sensing energy. [36] provides longest idle time of unused spectrum to the unlicensed user by using machine learning technique. Four supervised machine learning techniques (two from ANN and two from SVM) are used to investigate the prediction of length of the off period of the primary user.…”

Section: Machine Learning Models/techniques Proposed For Implementatimentioning

confidence: 99%

A Survey on Cognitive Radio Network using Artificial Neural Network

Waqar

Khadim²,

Zeb³

et al. 2017

IJFGCN

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“…From the Simulation results it is observed that performance of Perceptron Neural Network is better than Feed Forward Neural Network and Elman Neural Network. In [17], the authors compared four supervised learning algorithms, two from ANN, i.e. Multilayer Perceptron & Recurrent Neural Networks, and two from Support Vector Machines (SVM), i.e.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Comparative Analysis of ANN Techniques for Predicting Channel Frequencies in Cognitive Radio

Khan¹,

Wasi²,

Waqar³

et al. 2017

ijacsa

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Abstract-Demand of larger bandwidth increases the spectrum scarcity problem. By using the concepts of Cognitive radio we can achieve an efficient spectrum utilization. The cognitive radio allows the unlicensed user to share the licensed user band. To sense the accessibility of vacant channel and allocation of licensed user band is provided by Machine learning techniques because this decision need to be very fast and accurate. It is based on certain factors (such as Power, Bandwidth, antenna parameters, etc.). In this paper, we used neural network to propose this decision of resource allocation more accurately by providing bandwidth, power, antenna gain, azimuth, angle of elevation and location as a supplements factors to increase the predicting accuracy of Available channel frequencies for secondary user in particular bands. The comparative analysis is done between artificial neural network techniques to determine the maximum decision accuracy in order to design a suitable neural network structure and the system to make fast prediction for available channels. The dataset is divided in to cellular 850 MHZ and Advanced wireless service 1900/2100 MHZ bands. In both bands, Feed Forward networks performs better as compared to Elman and Radial basis network for predicting the best available channel to accommodate the secondary user. It will considerably increase overall QoS and decrease interference, hence making Cognitive radio system reliable.

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Learning based primary user activity prediction in cognitive radio networks for efficient dynamic spectrum access

Cited by 61 publications

References 1 publication

Dynamic Spectrum Sharing in 5G Wireless Networks With Full-Duplex Technology: Recent Advances and Research Challenges

Dynamic Spectrum Sharing in 5G Wireless Networks With Full-Duplex Technology: Recent Advances and Research Challenges

A Survey on Cognitive Radio Network using Artificial Neural Network

Comparative Analysis of ANN Techniques for Predicting Channel Frequencies in Cognitive Radio

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