Study On Distributed Energy-Saving Mechanism In LTE Network

With the continuous growth of wireless cellular network, the impact of energy consumption on the economy, society, environment and other aspects becomes more and more big, and its importance and urgency are increasingly prominent. Both the equipment providers and the network operators take the energy-efficiency into their consideration as a major design goal. They also begin to explore the new ways for energy-saving lately. Therefore, our study is a hot topic with both the necessity and importance.From the point of the mobile network’s development trend, this paper mainly studies the energy-saving mechanism under the heterogeneous LTE network. It also focuses on the distributed algorithm that can be easier realized for practical application. Thus we can reduce the base station power consumption with a guarantee of the network coverage and throughput.In this paper, we develop a theoretical framework for base station energy-saving that encompasses many technologys to form a system strategy, including beam-forming, collaboration of control channel, cell clustering and dynamic sleeping operation. Our clustering scheme based on the geographic information and traffic load is able to degrade the overall scale, also adds little additional signaling by the "cluster head" communication. Moreover, the base station sleeping policy could be found quickly through the dynamic programming algorithm with a state-reduced solution space. So the combination of these aspects allows for a more flexible and accessible tradeoff between the performance of cellular network and energy consumption with a lower algorithm complexity.The main contributions of this paper can be concluded as follows:1) the hierarchical energy-saving mechanism. It can reduce the system energy consumption effectively via the combination of different levels energy-saving algorithm.2) Lower computational complexity. Combining the idea of cell clustering with our state merging solution based on the load level, we narrow the range of using dynamic programming by several times.3) Good scalability. Based on the total cost of the actual network, our energy saving strategy has a wide range of applications covering different scenarios. It also makes the distributed computing and parallel operation among each cluster become possible.The simulation results under practical configurations demonstrate that the proposed dynamic algorithms can effectively reduce energy consumption at a low complexity, especially in the uneven business distribution in heterogeneous scenarios. So our strategy enables us to acquire about10%benefits in some scenes compared with the traditional method with the tradeoff between network performance and energy consumption.