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The cloud radio access network (C-RAN) is a promising paradigm to meet thestringent requirements of the fifth generation (5G) wireless systems.Meanwhile, wireless traffic prediction is a key enabler for C-RANs to improveboth the spectrum efficiency and energy efficiency through load-aware networkmanagements. This paper proposes a scalable Gaussian process (GP) framework asa promising solution to achieve large-scale wireless traffic prediction in acost-efficient manner. Our contribution is three-fold. First, to the best ofour knowledge, this paper is the first to empower GP regression with thealternating direction method of multipliers (ADMM) for parallel hyper-parameteroptimization in the training phase, where such a scalable training frameworkwell balances the local estimation in baseband units (BBUs) and informationconsensus among BBUs in a principled way for large-scale executions. Second, inthe prediction phase, we fuse local predictions obtained from the BBUs via across-validation based optimal strategy, which demonstrates itself to bereliable and robust for general regression tasks. Moreover, such across-validation based optimal fusion strategy is built upon a wellacknowledged probabilistic model to retain the valuable closed-form GPinference properties. Third, we propose a C-RAN based scalable wirelessprediction architecture, where the prediction accuracy and the time consumptioncan be balanced by tuning the number of the BBUs according to the real-timesystem demands. Experimental results show that our proposed scalable GP modelcan outperform the state-of-the-art approaches considerably, in terms ofwireless traffic prediction performance.

Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification