Optimized Small Cell Range Expansion in Mobile Communication Networks using Multi-Class Support Vector Machines

摘要

Heterogeneous cellular architectures are a promising technology direction for upcoming generations of wireless communication networks. Increasing performance requirements are fulfilled by utilizing a dense deployment of low-power small cells in addition to existing macro cells. In such dense cellular networks it is critical to prevent performance losses from increasing interferences and uneconomic operating costs caused by high power consumptions. These changes in the network architecture create the need for effective control mechanisms specifically designed for heterogeneous networks. Range expansion for small cells has been proposed and extensively researched to achieve load balancing between macro cells and small cells. In this work, we propose a decentralized approach for cell range expansion in small cell networks that in operation only requires very limited local interaction between neighboring cells. We use multiclass support vector machines as a classifier to select suitable parameters for each small cell. Experimental results show that the proposed decentralized approach achieves close to optimal load balancing performance.