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Sensors 2018, 18(9), 3000;

Efficient Interference Estimation with Accuracy Control for Data-Driven Resource Allocation in Cloud-RAN

1,2,3,* , 4
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China
Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19121, USA
This work was presented partially at Zhao, Y.; Wu, J.; Lu, S. Efficient SINR Estimating with Accuracy Control in Large Scale Cognitive Radio Networks. In Proceedings of the 2011 IEEE 17th International Conference on Parallel and Distributed Systems, Tainan, Taiwan, 7–9 December 2011, and Zhao, Y.; Li, W.; Wu, J.; Lu, S. Efficient RSS measurement in wireless networks based on compressive sensing. In Proceedings of the 2015 IEEE 34th International Performance Computing and Communications Conference (IPCCC), Nanjing, China, 14–16 December 2015.
Author to whom correspondence should be addressed.
Received: 20 July 2018 / Revised: 29 August 2018 / Accepted: 5 September 2018 / Published: 7 September 2018
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The emerging edge computing paradigm has given rise to a new promising mobile network architecture, which can address a number of challenges that the operators are facing while trying to support growing end user’s needs by shifting the computation from the base station to the edge cloud computing facilities. With such powerfully computational power, traditional unpractical resource allocation algorithms could be feasible. However, even with near optimal algorithms, the allocation result could still be far from optimal due to the inaccurate modeling of interference among sensor nodes. Such a dilemma calls for a measurement data-driven resource allocation to improve the total capacity. Meanwhile, the measurement process of inter-nodes’ interference could be tedious, time-consuming and have low accuracy, which further compromise the benefits brought by the edge computing paradigm. To this end, we propose a measurement-based estimation solution to obtain the interference efficiently and intelligently by dynamically controlling the measurement and estimation through an accuracy-driven model. Basically, the measurement cost is reduced through the link similarity model and the channel derivation model. Compared to the exhausting measurement method, it can significantly reduce the time cost to the linear order of the network size with guaranteed accuracy through measurement scheduling and the accuracy control process, which could also balance the tradeoff between accuracy and measurement overhead. Extensive experiments based on real data traces are conducted to show the efficiency of the proposed solutions. View Full-Text
Keywords: cloud-RAN; edge computing; resource allocation; interference measurement; modeling cloud-RAN; edge computing; resource allocation; interference measurement; modeling

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Zhao, Y.; Wu, J.; Li, W.; Lu, S. Efficient Interference Estimation with Accuracy Control for Data-Driven Resource Allocation in Cloud-RAN. Sensors 2018, 18, 3000.

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