Abstract
This paper focuses on interference coordination between the small cell and macro cell tiers of a wireless access network. We present a self-optimization mechanism for LTE-A eICIC parameters (CRE bias and ABS ratio) following a novel approach based on a model-free learning strategy, not requiring any previous knowledge about the network (e.g., topology, interference graph, and scheduling algorithms). Our proposal is built upon a stochastic optimization algorithm known as response surface methodology (RSM), that we use to find efficient eICIC configurations during network operation (online learning), adapting to changing network conditions, such as traffic or user distribution. The objective consists of optimizing a performance metric for which, in general, mathematical expression is unavailable. In particular, we consider the fifth percentile throughput defined by the 3GPP. By means of RSM, our mechanism obtains local approximations of the objective function to perform steepest ascent iterations with an adjustable level of statistical accuracy. The algorithm can be extended to account for stochastic constraints, allowing the network to optimize one performance metric while maintaining other metrics above a desired level.
