High-Accuracy Wireless Traffic Prediction: A GP-Based Machine Learning Approach
Yue Xu, Wenjun Xu, Feng Yin, Jiaru Lin, Shuguang Robert Cui · 2017
Wireless traffic prediction can effectively reduce the uncertainty in network demand and supply, and thus is a key enabler of smart management in next-generation wireless networks. To the best of our knowledge, this paper is the first to establish a wireless traffic prediction model by applying the Gaussian Process (GP) method based on real 4G traffic data. Our work is two-fold: First, based on the observed wireless traffic patterns, the kernel in our proposed GP model is designed accordingly to capture both the periodic trend and dynamic deviations; second, by leveraging the Toeplitz structure in the covariance matrix, the computational complexity of hyperparameter learning is significantly reduced from O(n3) to O(n2) and that of inference is reduced from O(n3) to O(n \log n), without any loss of prediction accuracy. Experimental results show that the proposed GP model can attain up to 97% prediction accuracy, and outperform the state-of-the-art algorithms considerably.