Learning-Aided Multiple Time-Scale SON Function Coordination in Ultra-Dense Small-Cell Networks

To satisfy the high requirements on operation efficiency in the 5G network, self-organizing network (SON) is envisioned to reduce the network operating complexity and costs by providing SON functions, which can optimize the network autonomously. However, different SON functions have different time scales and inconsistent objectives, which leads to conflicting operations and network performance degradation, raising the needs for SON coordination solutions. In this paper, we devise a multiple time-scale coordination management scheme (MTCS) for densely deployed SONs, considering the specific time scales of different SON functions. Specifically, we propose a novel analytical model named ${M}$ time-scale Markov decision process, where SON decisions made in each time-scale consider the impacts of SON decisions in other ${M}-{1}$ time scales on the network. Furthermore, in order to manage the network more autonomously and efficiently, a Q-learning algorithm for SON functions in the proposed MTCS scheme is proposed to achieve a stable control policy by learning from history experience. To improve energy efficiency, we then evaluate the proposed MTCS scheme with two functions of mobility load balancing and energy saving management with designed network utility. The simulation results show that the proposed SON coordination scheme significantly improves the network utility with different quality of experience requirements while guaranteeing stable operations in wireless networks.