Data-Driven Self-Organization With Implicit Self-Coordination for Coverage and Capacity Optimization in Cellular Networks

Abstract

Coverage and Capacity Optimization (CCO) and Inter-Cell Interference Coordination (ICIC) are two tightly coupled and conflicting Self-Organizing Network (SON) functions that are responsible for ensuring optimal coverage and capacity in any cellular network. While executing currently, these functions may modify the same RF and antenna parameters, resulting in severe performance deteriorations. In this context, a centralized optimization and coordination approach may be impractical considering the large sizes of network clusters and the dynamics involved between the several other defined SON use cases. In this work, an implicitly coordinated and scalable self-organizing architecture is followed such that when a carefully defined multi-objective utility function for CCO-ICIC joint optimization is optimized locally by each RAN node, a desired balance between the two conflicting network targets of coverage and capacity is ensured globally. Pareto analysis of three variants of the proposed Local Multi-Objective KPI (LMO KPI) has been conducted to implicitly coordinate the two SON functions in a distributed self-organized manner. In order to recommend appropriate network configurations dynamically to quickly adapt to altering network environments, two collaborative filtering-based Recommender Systems (RecSys), one using a Deep Autoencoder and another based on Singular Value Decomposition, have been employed along with a neural network regressor to improve recommendations for cold-start scenarios. The two proposed hybrid-RecSys-based SON coordination solutions, while adopting an appropriate Local Multi-Objective KPI (LMO KPI), outperform previous work in coverage by 36% and in capacity by around 2% while reducing power consumption by more than 50%. The study demonstrates that the definition of the LMO KPI is crucial to the performance of this approach. Altogether, the work shows that the adopted self-organization and implicit SON-coordination approach is not only feasible and performant but also scales well if implemented meticulously.