Self-Optimizing Mechanisms for EMF Reduction in Heterogeneous Networks

TL;DR

This paper introduces a self-optimizing algorithm that dynamically adjusts heterogeneous network parameters to minimize RF EMF exposure, balancing exposure reduction with quality of service through stochastic approximation and load balancing.

Contribution

It develops a novel stochastic approximation based self-optimizing algorithm for EMF exposure reduction in heterogeneous networks, with proven convergence and performance evaluation.

Findings

The algorithm effectively reduces EMF exposure in simulations.

Increasing small cell coverage can lower EMF exposure up to a limit.

Proper load balancing maintains QoS while minimizing exposure.

Abstract

This paper focuses on the exposure to Radio Frequency (RF) Electromagnetic Fields (EMF) and on optimization methods to reduce it. Within the FP7 LEXNET project, an Exposure Index (EI) has been defined that aggregates the essential components that impact exposure to EMF. The EI includes, among other, downlink (DL) exposure induced by the base stations (BSs) and access points, the uplink (UL) exposure induced by the devices in communication, and the corresponding exposure time. Motivated by the EI definition, this paper develops stochastic approximation based self-optimizing algorithm that dynamically adapts the network to reduce the EI in a heterogeneous network with macro- and small cells. It is argued that the increase of the small cells' coverage can, to a certain extent, reduce the EI, but above a certain limit, will deteriorate DL QoS. A load balancing algorithm is formulated that adapts the small cell' coverage based on UL loads and a DL QoS indicator. The proof of convergence of the algorithm is provided and its performance in terms of EI reduction is illustrated through extensive numerical simulations.