Minimum Bayesian Risk Based Robust Spectrum Prediction in Dynamic Spectrum Access
CHEN Xi① YANG Jian②
①(School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China) ②(Nanjing Telecommunication Technology Institute, Nanjing 210007, China)
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.
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