Minimum Bayesian Risk Based Robust Spectrum Prediction in Dynamic Spectrum Access

CHEN Xi; YANG Jian

doi:10.11999/JEIT170519

Volume 40 Issue 3

Mar. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(3): 734-742

CHEN Xi, YANG Jian. Minimum Bayesian Risk Based Robust Spectrum Prediction in Dynamic Spectrum Access[J]. Journal of Electronics & Information Technology, 2018, 40(3): 734-742. doi: 10.11999/JEIT170519

Citation:

CHEN Xi, YANG Jian. Minimum Bayesian Risk Based Robust Spectrum Prediction in Dynamic Spectrum Access[J]. Journal of Electronics & Information Technology, 2018, 40(3): 734-742. doi: 10.11999/JEIT170519

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Minimum Bayesian Risk Based Robust Spectrum Prediction in Dynamic Spectrum Access

doi: 10.11999/JEIT170519

CHEN Xi¹,
YANG Jian¹

(School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

Funds:

The National Natural Science Foundation of China (61471395, 61471392, 61301161), The Natural Science Foundation of Jiangsu Province (BK20141070)

Received Date: 2017-05-27
Rev Recd Date: 2017-11-29
Publish Date: 2018-03-19

Abstract

Abstract

The accumulation of miss detection and false alarm in spectrum sensing leads to the persistently decreasing of prediction accuracy in spectrum prediction. This paper takes neural network based spectrum prediction for example, and presents a minimum Bayesian Risk based spectrum prediction to solve this problem. The distribution fitting shows that the prediction output follows the normal distribution. The expectation of prediction mean square error is defined as the Bayesian Risk, and the optimal detection threshold of the prediction output is derived through minimizing the Bayesian Risk. Through this method, the prediction accuracy is insensitive to the spectrum sensing errors. Compared with the traditional spectrum prediction with fixed detection thresholds, simulation results demonstrate the robust spectrum prediction keeps the prediction accuracy stable, and improve the performance in dynamic spectrum access.
- Dynamic spectrum access,
- Robust spectrum prediction,
- Neural networks,
- Bayesian risk,
- Prediction accuracy

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References(15)

References

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