Machine Learning Based Uplink Transmission Power Prediction for LTE and Upcoming 5G Networks using Passive Downlink Indicators

TL;DR

This paper introduces a machine learning approach to predict uplink transmission power in LTE and 5G networks using passive indicators, aiding energy-efficient system design for IoT and mobile devices.

Contribution

It presents a novel ML-based method for uplink power prediction from passive network data, validated with real-world measurements, improving energy-aware communication system modeling.

Findings

Random-Forest achieved the best prediction accuracy with MAE of 3.166 dB.

Prediction errors decrease below 1 dB after 28 predictions on average.

The approach is suitable for long-term power estimation in cellular networks.

Abstract

Energy-aware system design is an important optimization task for static and mobile Internet of Things (IoT)-based sensor nodes, especially for highly resource-constrained vehicles such as mobile robotic systems. For 4G/5G-based cellular communication systems, the effective transmission power of uplink data transmissions is of crucial importance for the overall system power consumption. Unfortunately, this information is usually hidden within off-the-shelf modems and mobile handsets and can therefore not be exploited for enabling green communication. Moreover, the dynamic transmission power control behavior of the mobile device is not even explicitly modeled in most of the established simulation frameworks. In this paper, we present a novel machine learning-based approach for forecasting the resulting uplink transmission power used for data transmissions based on the available passive network quality indicators and application-level information. The model is derived from comprehensive field measurements of drive tests performed in a public cellular network and can be parameterized for integrating all measurements a given target platform is able to provide into the prediction process. In a comparison of three different machine learning methods, Random-Forest models thoroughly performed best with a mean average error of 3.166 dB. As the absolute sum of errors converges towards zero and falls below 1 dB after 28 predictions in average, the approach is well-suited for long-term power estimations.