Recently, there are many studies, that considering green wireless cellular networks, have taken the energy consumption of the base station (BS) into consideration. In this work, we first introduce an energy consumption model of multi-mode sharing BS powered by multiple energy sources including renew...
Recently, there are many studies, that considering green wireless cellular networks, have taken the energy consumption of the base station (BS) into consideration. In this work, we first introduce an energy consumption model of multi-mode sharing BS powered by multiple energy sources including renewable energy, local storage and power grid. Then communication load requests of the BS are transformed to energy demand queues, and battery energy level and worst-case delay constraints are considered into the virtual queue to ensure the network QoS when our objective is to minimize the long term electricity cost of BSs. Lyapunov optimization method is applied to work out the optimization objective without knowing the future information of the communication load, real-time electricity market price and renewable energy availability. Finally, linear programming is used, and the corresponding energy efficient scheduling policy is obtained. The performance analysis of our proposed online algorithm based on real-world traces demonstrates that it can greatly reduce one day's electricity cost of individual BS.
Recently, there are many studies, that considering green wireless cellular networks, have taken the energy consumption of the base station (BS) into consideration. In this work, we first introduce an energy consumption model of multi-mode sharing BS powered by multiple energy sources including renewable energy, local storage and power grid. Then communication load requests of the BS are transformed to energy demand queues, and battery energy level and worst-case delay constraints are considered into the virtual queue to ensure the network QoS when our objective is to minimize the long term electricity cost of BSs. Lyapunov optimization method is applied to work out the optimization objective without knowing the future information of the communication load, real-time electricity market price and renewable energy availability. Finally, linear programming is used, and the corresponding energy efficient scheduling policy is obtained. The performance analysis of our proposed online algorithm based on real-world traces demonstrates that it can greatly reduce one day's electricity cost of individual BS.
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문제 정의
Therefore, how to reduce the electricity bills attracts more interest for cellular operators. In this paper, we investigate the forthcoming multi-mode sharing base station and exploit the unquie characteristics of cellular IOT service to reduce the electricity bills. To the best of my knowledge, cellular IoT services can be divided into delay-sensitive and delay-tolerant tasks.
where the left part is the growth of the queue and the right part is the expected electricity cost for the communication load of the BS. The objective is to minimize the weighted sum of drift and penalty (cost), which can be proven that it holds. The following inequality holds:
성능/효과
Finally, the solution to the proposed optimal problem is achieved to form the corresponding energy efficient scheduling policy. Simulation results, which are based on real data, show that our proposed online algorithm can greatly reduce the electricity cost of per BS by 68.63%. Future work is in progress to consider how to choose an optimal battery capacity and prolong the lifetime of storage battery, on which the times of charging and discharging have an obvious effect.
The result shows that the communication electricity cost of multi-mode BS of one day is 14.8 when using Lyapunov optimization and choosing the appropriate value for parameter V and η, while 47.19 without renewable energy and 21.65 without introducing Lyapunov optimization algorithm, which means there is no deferrable task.
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