Scheduling of small cells in Fifth-Generation (5G) mobile network is highly important for achieving energy-efficiency and providing Quality of Service (QoS) to the applications users. Minimization of energy consumption hampers QoS. This problem has been further complicated due to exponential increase of mobile application users demanding high data rate. The performances of energy-saving approaches in the literature are limited by the fact that they exploit mere historical data-driven two state operation modes of small cells. This paper formulates the problem of scheduling small cells as a non-linear optimization problem. It then offers a meta-heuristic evolutionary algorithm to solve the problem in polynomial time. The proposed algorithm takes into account four operation states of small cells to minimize the energy consumption while satisfying the users’ QoS. The results of our performance analysis depict that the proposed algorithm outperforms the state-of-the-art works in terms of energy-saving, switching delay, etc.

第五代（5G）移动网络中的小型小区调度对于实现能源效率和向应用程序用户提供服务质量（QoS）至关重要。能耗最小化会影响QoS。由于要求高数据速率的移动应用用户的指数级增长，这个问题变得更加复杂。节能方法在文献中的表现受到以下事实的限制，即它们仅利用历史数据驱动的小型单元的两种状态操作模式。本文将调度小型单元的问题表述为非线性优化问题。然后，它提供了一种元启发式进化算法来解决多项式时间内的问题。所提出的算法考虑了小小区的四个操作状态，以在满足用户QoS的同时最大程度地降低能耗。我们的性能分析结果表明，该算法在节能，切换延迟等方面均优于最新技术。