Joint Optimization of Coverage, Capacity and Load Balancing in Self-Organizing Networks

TL;DR

This paper presents a joint optimization framework for self-organizing networks that enhances coverage, capacity, and load balancing through a novel two-step iterative scheme and uplink-downlink duality analysis.

Contribution

It introduces a unified optimization approach for SON functionalities using monotone and subhomogeneous functions, including a duality-based solution for the downlink problem.

Findings

Achieves a good trade-off between coverage, capacity, and load balancing.

Proposes a two-step fixed point iteration scheme for uplink optimization.

Demonstrates the effectiveness of the approach through simulations.

Abstract

This paper develops an optimization framework for self-organizing networks (SON). The objective is to ensure efficient network operation by a joint optimization of different SON functionalities, which includes capacity, coverage and load balancing. Based on the axiomatic framework of monotone and strictly subhomogeneous function, we formulate an optimization problem for the uplink and propose a two-step optimization scheme using fixed point iterations: i) per base station antenna tilt optimization and power allocation, and ii) cluster-based base station assignment of users and power allocation. We then consider the downlink, which is more difficult to handle due to coupled variables, and show downlink-uplink duality relationship. As a result, a solution for the downlink is obtained by solving the uplink problem. Simulations show that our approach achieves a good trade-off between coverage, capacity and load balancing.