# Transmit power optimization in cellular networks with nomadic base stations

## TL;DR

This paper proposes a sampling-based local transmit power reconfiguration method for cellular networks with nomadic base stations, effectively reducing interference with minimal reconfiguration of neighboring cells.

## Contribution

It introduces a novel, sampling-based local power optimization algorithm that requires reconfiguring fewer cells while maintaining near-optimal network throughput.

## Key findings

- Reconfigures fewer than 10 neighboring cells in most cases
- Achieves less than 1% difference in total throughput compared to global optimization
- Effective in both regular and realistic city topologies

## Abstract

The increasing demand for cellular network capacity can be mitigated through the installation of nomadic eNodeB, which serve a temporal increase of traffic volume in specific area. When nomadic cells are deployed, the transmission power of neighbor base stations needs to be optimized to limit the inter-cell interferences. We analyze the problem of neighborhood selection for the optimization, to define what part of the networks needs to be reconfigured when new base station is added. We evaluate the iterative approach, with increasing range of neighboring cells being reconfigured and propose a novel, sampling based local TX power reconfiguration method, which is evaluated by a numerical model in both regular (honeycomb) topology and in realistic topology reflecting locations of cells in a city. The analysis confirms that the proposed algorithm allows to select very few neighboring cells which need to be reconfigured (in majority of the cases less than 10 cells) and achieve similar efficacy as global optimization, with total network throughput different by less than 1\% comparing to the global optimization.