期刊
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
卷 37, 期 6, 页码 1291-1306出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2019.2904330
关键词
C-RANs; Gaussian processes; parallel processing; ADMM; cross-validation; machine learning; wireless traffic
资金
- National Natural Science Foundation of China [61629101, 61771066]
- NSF [DMS-1622433, AST-1547436, ECCS-1659025]
- Guangdong Province [2017ZT07X152]
- Shenzhen Fundamental Research Fund [KQTD2015033114415450, ZDSYS201707251409055, JCYJ20170307155957688]
The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (SG) wireless systems. Meanwhile, the wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This paper proposes a scalable Gaussian process (GP) framework as a promising solution to achieve large-scale wireless traffic prediction in a cost-efficient manner. Our contribution is three-fold. First, to the hest of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions. Second, in the prediction phase, we fuse local predictions obtained from the BBUs via a cross-validation-based optimal strategy, which demonstrates itself to be reliable and robust for general regression tasks. Moreover, such a cross-validation-based optimal fusion strategy is built upon a well acknowledged probabilistic model to retain the valuable closed-form GP inference properties. Third, we propose a C-RAN-based scalable wireless prediction architecture, where the prediction accuracy and the time consumption can be balanced by tuning the number of the BBUs according to the real-time system demands. The experimental results show that our proposed scalable GP model can outperform the state-of-the-art approaches considerably, in terms of wireless traffic prediction performance.
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