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.
Highlights
With the rapid increase in mobile data traffic, cellular networks are expected to provide high-capacity services to users
We propose an remote radio head (RRH) switching operation based on Markov mobility prediction and optimize the transmission power based on a gradient method
To improve energy efficiency in heterogeneous cloud radio access network (H-CRAN) with high mobility environments, we propose that an energy-efficient power algorithm with mobility prediction provides RRH switching operation based on the predicted mobility
Summary
With the rapid increase in mobile data traffic, cellular networks are expected to provide high-capacity services to users. In order to satisfy the requirements, cellular networks have been studied and many network architectures have been developed. One base station (BS) is deployed in one cell structure. In order to improve the original system bandwidth and performance, many architectures have been studied for future 5G cellular networks. As a larger data capacity is provided, the energy consumption has become an important issue. The data capacity and energy consumption exhibit a “trade-off” relationship. In high-capacity services, the energy consumption from the BS side corresponds to approximately 75% of the total energy consumption in cellular networks [1]. Considering the increasing energy consumption, studies are performed for an effective and minimized energy consumption
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