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Sensors 2018, 18(9), 2904; https://doi.org/10.3390/s18092904

Energy-Effective Power Control Algorithm with Mobility Prediction for 5G Heterogeneous Cloud Radio Access Network

Department of IT Engineering, Sookmyung Women’s University, Seoul 04310, Korea
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Received: 26 July 2018 / Revised: 29 August 2018 / Accepted: 29 August 2018 / Published: 1 September 2018
(This article belongs to the Special Issue Advances on Vehicular Networks: From Sensing to Autonomous Driving)
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Abstract

In 5G networks, heterogeneous cloud radio access network (H-CRAN) is considered a promising future architecture to minimize energy consumption and efficiently allocate resources. However, with the increase in the number of users, studies are performed to overcome the energy consumption problems. In this study, we propose a power control algorithm with mobility prediction to provide a high-energy efficiency for 5G H-CRAN. In particular, the proposed algorithm predicts UE mobility in vehicular mobility scenarios and performs remote radio head (RRH) switching operations based on % prediction results. We formulate an optimization problem to maximize the energy efficiency while satisfying the outage probability requirement. We then propose an RRH switching operation based on Markov mobility prediction and optimize the transmission power based on a gradient method. Simulation results demonstrate the improved energy efficiency compared with those of existing RRH switching-operation algorithms. View Full-Text
Keywords: 5G; heterogeneous cloud radio access network; vehicular mobility; remote radio head switching operation 5G; heterogeneous cloud radio access network; vehicular mobility; remote radio head switching operation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Park, H.; Lim, Y. Energy-Effective Power Control Algorithm with Mobility Prediction for 5G Heterogeneous Cloud Radio Access Network. Sensors 2018, 18, 2904.

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