Ericsson Careers

Master thesis: Cognitive Automation Lab - Autonomous radio tower validation using drones

Stockholm, Sweden
Information Technology

English (US)

Job Description

Date: Nov 6, 2018

Machine Intelligence and Automation is a research area at Ericsson Research. Our mission is to drive machine intelligence thought leadership to automate and transform Ericsson offerings and operations and to enable emerging business. This includes development of models, frameworks and infrastructure where we in our advancements push the technology frontiers. We engage in both academic and industry collaborations and drive the digitalization of Ericsson and the Industry by developing state of the art solutions that simplify and automate processes in our products and services and create new value through data insights.

The Cognitive Automation Lab group at Ericsson Research in Stockholm is a research group within the Machine Intelligence and Automation research area working on all aspects of artificial intelligence, such as knowledge representation, deduction, reasoning and problem solving, image processing, natural language processing, automated planning and applied machine learning. We are currently looking for several master thesis students that will work on different problems that can improve servisability of "radio sites", i.e. areas where Radio Base Stations are installed using drones. In particular, radio tower validation during site commissioning and subsequent inspection during a radio site’s lifetime is an expensive and dangerous process, as it involves physical presence of field technicians at remote radio sites, that are also required to climb up towers. Recently, and in order to offset some of the safety concerns, piloted drones have been commissioned to survey radio towers. However, these drones still require the presence of a qualified pilot and radio site engineer, so they can be piloted up the tower, take video of the components of the tower, so that the radio engineer can subsequently evaluate the results.

At Ericsson Research we are adding intelligence to the drones, so they are not only able to self-evaluate a radio tower installation but also be able to fly on top of the tower without a human pilot. We are therefore looking for several talented students to work on the following areas:
  • Detection of components in the tower (e.g. radio units, antennas, etc.).
  • Real-time diagnosis of faults with radio equipment based on video feed from the drone and other sensors (e.g. depth of field, infrared, etc.).
  • Autonomous navigation using knowledge graph of Places of Interest (POI) on a radio tower
Every successful candidate will be given a specific problem to work on in one of the areas above but is expected to collaborate with other successful candidates towards a single solution. We are therefore looking for candidates that exhibit the following qualities and skillset:
  • Willingness to take on new challenges, ability to work in small teams, “can do” attitude
  • Programming experience in Java or C++ is a requirement
  • Experience with machine learning (object detection), image processing (morphological transformation, edge detection, background subtraction) and AI (knowledge formalization and planning) is a plus
  • This thesis will focus on improving serviceability of "radio sites", i.e. areas where Radio Base Stations are installed. Current state of art in radio site maintenance, involves skilled technicians visiting the sites for inspection and troubleshooting, at a large cost. This thesis, will develop a system where a dispatched unmanned aerial vehicle (UAV) performs a diagnosis of the radio site, and reports to it's dispatcher whether on-site human presense is required. Parts required to build the solution, such as object detection models of radio site equipment and means to interface to the equipment will be offered, but the successful candidate is required to integrate them to the overall system.


            Contact persons are: Athanasios Karapantelakis

                                            Maxim Teslenko:

                                            Yifei Jin:

                                            Marin Orlic:

                                            Leonid Mokrushin:



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Primary country and city: Sweden (SE) || || Stockholm || IT

Req ID: 261097