Project description
This project contributes to the development of drone-based base stations that can position themselves to provide network connectivity in areas where it is either unavailable or insufficient.
While households in industrialized countries enjoy high data transmission speeds through optical fiber cables and 4G/5G-based wireless communication, nearly half of the world's population lacks access to the internet. Improving internet availability in such areas would not only have economic benefits but also a positive impact on the Internet of Things, smart agriculture and forestry, wildfire prevention, search and rescue operations, first aid, and crisis management.
With this in mind, the UAVs in the proposed technical solution are equipped with a communication module that connects to the user on one side and to mobile land-based infrastructure on the other. User data can also be relayed through multiple UAVs before reaching its destination. For this technology to be viable, the UAVs must be able to navigate autonomously to locations with optimal transmission and reception conditions. This means they must identify positions where the signals transmitted and received between the user and the mobile land-based infrastructure are not obstructed by stationary obstacles such as buildings or mountains.
The main approach in this project is to construct radio maps that describe the signal conditions in a given region. By utilizing these maps, UAVs can leverage artificial intelligence algorithms to determine the optimal location for each UAV. These algorithms can autonomously adapt to changes in user positions and connectivity needs while also coordinating their placement with other UAVs.
So far, our results include a significant set of contributions on three levels: fundamental, algorithmic, and empirical.
- On the fundamental level, we were the first group in the world to analyze the radio map estimation problem from an abstract mathematical perspective and derive performance limits for simple estimators. This allows us to predict in advance whether a radio map estimate will be accurate or not.
- On the algorithmic level, we developed multiple types of algorithms, primarily for UAV path planning to efficiently collect measurements for radio map estimation (spectrum measurement) and for determining the optimal positioning of aerial base stations and relays using radio maps. Based on theoretical insights, we also developed cybersecurity algorithms to detect the presence of spoofing attacks and methods for selecting beamformers in real-world environments where location estimates may be inaccurate.
- On the empirical level, we were the first group worldwide to comprehensively investigate the radio map estimation problem from an empirical perspective. Our customized system enabled us to collect measurements from over 300,000 locations using a UAV, providing our results with high statistical significance.
