# Boosting Vehicle-to-cloud Communication by Machine Learning-enabled Context Prediction

## TL;DR

This paper introduces a machine learning-based transmission scheme for vehicle-to-cloud communication that predicts future channel states to optimize data transmission, significantly improving data rates and reducing power consumption in LTE networks.

## Contribution

It presents a novel machine learning-enabled approach for opportunistic data transmission that considers future channel behavior to enhance resource efficiency in MTC.

## Key findings

- Mean data rate increased by 194%
- Average power consumption reduced by up to 54%
- Effective in real LTE network field evaluations

## Abstract

The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. However, the massive increases in Machine-type Communication (MTC) highly stress the capacities of the network infrastructure. With the system-immanent limitation of resources in cellular networks and the resource competition between human cell users and MTC, more resource-efficient channel access methods are required in order to improve the coexistence of the different communicating entities. In this paper, we present a machine learning-enabled transmission scheme for client-side opportunistic data transmission. By considering the measured channel state as well as the predicted future channel behavior, delay-tolerant MTC is performed with respect to the anticipated resource-efficiency. The proposed mechanism is evaluated in comprehensive field evaluations in public Long Term Evolution (LTE) networks, where it is able to increase the mean data rate by 194% while simultaneously reducing the average power consumption by up to 54%.