Dynamic Load Adjustments for Small Cells in Heterogeneous Ultra-dense Networks

Abstract

The ultra-dense deployment of small cells has been applied to the 5th-generation (5G) mobile networks. A large number of base stations (BSs) will lead to a dramatic increase in energy consumption, and network resources will be more difficult to fully utilize. In this paper, we propose the dynamic load adjustments (DLA) algorithm for small cells in heterogeneous ultra-dense networks. The proposed algorithm applies Q-learning to learn effective offloading policies which could combine the energy-saving function and the load balancing function. Based on the DLA algorithm, the heterogeneous ultra-dense networks could adjust the traffic load to turn off some redundant BSs or balance the load between heavily loaded BSs and lightly loaded BSs. The simulation results show that the algorithm not only improves the network energy efficiency when the average load of the networks is light, but also improves the network throughput when the average load of the networks is heavy.