A Energy Prediction Based Spectrum Sensing Approach for Cognitive Radio Networks

Lin, Zhijian; Jiang, Xueyuan; Huang, Lianfen; Yao, Yan

doi:10.1109/wicom.2009.5302514

Cited by 29 publications

(17 citation statements)

References 4 publications

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“…The researcher did not, however, include any learning from past experience or historical data. An exponential moving average (EMA) spectrum sensing using energy prediction was implemented in [21]. The EMA achieved a prediction average mean square error (MSE) of 0.2436 with the assumption that the channel utilization follow exponential distribution with rate parameter λ = 0.2 and signal to noise (SNR) of 10dB; RF real world data was not used in their study.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Application of Artificial Neural Network and Support Vector Regression in Cognitive Radio Networks for RF Power Prediction Using Compact Differential Evolution Algorithm

Iliya¹,

Goodyer²,

Gow³

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. To enhance the selection of channel with less noise among the white spaces (idle channels), the a priory knowledge of Radio Frequency (RF) power is very important. Computational Intelligence (CI) techniques cans be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) and Support Vector Regression (SVR) models for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) FM and TV bands. Sensitivity analysis was used to reduce the input vector of the prediction models. The inputs of the ANN and SVR consist of only time domain data and past RF power without using any RF power related parameters, thus forming a nonlinear time series prediction model. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters such as signal to noise ratio, bandwidth and bit error rate. Since CR are embedded communication devices with memory constrain limitation, the models used, implemented a novel and innovative initial weight optimization of the ANN's through the use of compact differential evolutionary (cDE) algorithm variants which are memory efficient. This was found to enhance the accuracy and generalization of the ANN model.

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Section: Related Workmentioning

confidence: 99%

“…The crossover rate Cr is the probability of selecting the offspring genes from the mutant while j rand is a random number in the range [1 D], this ensure that at least one of the offspring gene is copied from the mutant. The binomial crossover is represented by (21), [32]:…”

Section: Cross Overmentioning

confidence: 99%

Application of Artificial Neural Network and Support Vector Regression in Cognitive Radio Networks for RF Power Prediction Using Compact Differential Evolution Algorithm

Iliya¹,

Goodyer²,

Gow³

et al. 2015

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…An exponential moving average (EMA) spectrum sensing using energy prediction was implemented in [14]. The EMA achieved a prediction average error of 0.2436 with the assumption that the channel utilization follow exponential distribution with rate parameter =0.2 and signal to noise (SNR) of 10dB; RF real world data was not used in their study.…”

Section: Related Workmentioning

confidence: 99%

Optimized artificial neural network using differential evolution for prediction of RF power in VHF/UHF TV and GSM 900 bands for cognitive radio networks

Iliya

Goodyer

Góngora

et al. 2014

2014 14th UK Workshop on Computational Intelligence (UKCI)

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Abstract-Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The knowledge of Radio Frequency (RF) power (primary signals and/ or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance, not just the existence or absence of primary users. If a channel is known to be noisy, even in the absence of primary users, using such channels will demand large quantities of radio resources (transmission power, bandwidth, etc) in order to deliver an acceptable quality of service to users. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). While most of the prediction schemes are based on the determination of spectrum holes, those designed for power prediction use known radio parameters such as signal to noise ratio (SNR), bandwidth, and bit error rate. Some of these parameters may not be available or known to cognitive users. In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters. The models used implemented a novel and innovative initial weight optimization of the ANN's through the use of differential evolutionary algorithms. This was found to enhance the accuracy and generalization of the approach

show abstract

“…The researcher did not, however, include any learning from past experience or historical data. An exponential moving average (EMA) spectrum sensing using energy prediction was implemented in [16]. The EMA achieved a prediction average error of 0.2436 with the assumption that the channel utilization follow exponential distribution with rate parameter =0.2 and signal to noise (SNR) of 10dB; RF real world data was not used in their study.…”

Section: Related Workmentioning

confidence: 99%

Optimized Neural Network using differential evolutionary and swarm intelligence optimization algorithms for RF power prediction in cognitive radio network: A comparative study

Iliya

Goodyer

Shell

et al. 2014

2014 IEEE 6th International Conference on Adaptive Science &Amp; Technology (ICAST)

View full text Add to dashboard Cite

Abstract-Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The a priory knowledge of Radio Frequency (RF) power (primary signals and/ or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance. This will enable the selection of channel with less noise among idle (free) channels. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters such as signal to noise ratio, bandwidth and bit error rate. The models used, implemented a novel and innovative initial weight optimization of the ANN's through the use of differential evolutionary and swarm intelligence algorithms. This was found to enhance the accuracy and generalization of the ANN model. For this problem, DE/best/1/bin was found to yield a better performance as compared with the other algorithms implemented.

show abstract

A Energy Prediction Based Spectrum Sensing Approach for Cognitive Radio Networks

Cited by 29 publications

References 4 publications

Application of Artificial Neural Network and Support Vector Regression in Cognitive Radio Networks for RF Power Prediction Using Compact Differential Evolution Algorithm

Application of Artificial Neural Network and Support Vector Regression in Cognitive Radio Networks for RF Power Prediction Using Compact Differential Evolution Algorithm

Optimized artificial neural network using differential evolution for prediction of RF power in VHF/UHF TV and GSM 900 bands for cognitive radio networks

Optimized Neural Network using differential evolutionary and swarm intelligence optimization algorithms for RF power prediction in cognitive radio network: A comparative study

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