Frequency allocation with Artificial Neural Networks in cognitive radio system

Tan, Xuezhi; Huang, Haoqin; Ma, Lin

doi:10.1109/tenconspring.2013.6584473

Cited by 12 publications

(11 citation statements)

References 8 publications

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“…The ANN gets information from the neighboring neurons and gives an output depending on its weights and functions. Tan et al, (2013) found the cons of spectrum lack and inefficient in the communication networks and the by introducing new solutions using ANN by replacing the current frequency allocation system.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A Comprehensive Review on Artificial Intelligence Based Machine Learning Techniques for Designing Interactive Characters

Nawalagatti¹,

Tjprc²

2018

IJMCAR

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The recent progress in artificial intelligence based machine learning techniques has renewed interest in building and designing the interactive characters. To achieve optimal performance many techniques have been used along with the neural networks, which are prepared end to end in tasks such as object recognition video games and board games. In this paper, a review of current literature is carried out where the challenges in real time strategy game and the tasks in a real-time environment are explored. The comparative analysis is made on different AI based machine learning techniques to comprehend the performance issues and other challenges faced. Furthermore, discussion of the advantages and disadvantages of each technique is conducted so as to understand and compare the efficiencies with the existing techniques based on the strategies.

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Section: Artificial Neural Networkmentioning

confidence: 99%

A Comprehensive Review on Artificial Intelligence Based Machine Learning Techniques for Designing Interactive Characters

Nawalagatti¹,

Tjprc²

2018

IJMCAR

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show abstract

“…In this paper, the author used the Combination of Cognitive Radio and Back Propagation (BP) Artificial Neural Network (ANN) both together for the replacement of Complicated Frequency allocation, from the Simulation author comparing Back Propagation (BP) Artificial Neural Network (ANN) with the original Frequency Allocation which is able to give out the accurate simulation results which means that the BP ANN gave the solution of Complicated frequency allocation and by using this method we will get accurate results of frequency allocation which also increases the Quality of Service and reduces the interference [42].…”

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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“…In [21], the authors introduces an artificial neural network based learning scheme for predicting data rate for specific radio configuration and used the algorithm for estimating the performance of data rate and throughput. In [22], the author the Combination of Cognitive Radio and Feed Forward Back Propagation both together for the replacement of Complicated Frequency allocation, from the Simulation author comparing Back Propagation (BP) Artificial Neural Network (ANN) with the original Frequency Allocation which is able to give out the accurate simulation results which means that the BP ANN gave the solution of Complicated frequency allocation and by using this method we will get accurate results of frequency allocation which also increases the Quality of Service and reduces the interference.…”

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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Frequency allocation with Artificial Neural Networks in cognitive radio system

Cited by 12 publications

References 8 publications

A Comprehensive Review on Artificial Intelligence Based Machine Learning Techniques for Designing Interactive Characters

A Comprehensive Review on Artificial Intelligence Based Machine Learning Techniques for Designing Interactive Characters

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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