What will be your role?
Vehicle-to-everything (V2X) communication is key application area of 5G, with the motivations to enhance road safety, traffic efficiency and environmental sustainability (considering the energy usage and emission of vehicles). V2X applications require 5G network technology to support both Ultra-Reliable Low-Latency Communication (URLLC) services and non-URLLC services. The typically highly varying V2X communication environment, due to potential high-speed mobility of the vehicles, poses significant challenges for 5G to fulfil the requirements of the diverse V2X applications.
Beamforming and massive single/multi-user MIMO technologies are important 5G components to increase coverage, link quality and network capacity by steering radio signals towards targeted users and avoiding interference in unwanted directions. Adaptive beamforming is desired in V2X communications in order to dynamically react to the changes in radio propagations due to movement of the vehicles in terms of timely selecting the best possible beam direction and beam width, considering the impact of the latter on both the effective antenna gain and the robustness of the transmission. Such adaptivity comes at an overhead cost of (often frequent) control signalling via the air interface. It may be promising to develop Machine Learning (ML)-based beamforming optimization approaches striking an optimal trade-off between performance, complexity and signalling overhead.
The key objectives of the proposed graduation project are:
- To identify V2X scenarios where adaptive beamforming is applicable and analyse the-state-of-the-art (SoTA) (particularly also ML-based) adaptive beamforming solutions in determining the most suitable precoder for beamforming.
- To propose one or more ML-based beamforming optimization approaches, which tackles identified challenges in potentially high-speed V2X communications and outperforms SoTA approaches in flexibility and the attainability of a good trade-off between performance, complexity and signalling overhead.
- The quantitative assessment of the proposed ML-based beamforming optimization approaches, comprising a mutual comparison and a benchmark against key SoTA algorithms, as well as a sensitivity analysis w.r.t. key scenario aspects, in order to demonstrate attainable gains and identify the best candidate. This requires the specification of suitable and realistic scenarios in terms of system, propagation and traffic aspects and the development/application of a simulation tool to do the assessment.
The graduation project is carried out as part of the European Horizon 2020 5G-HEART project. You will learn about the broader context of the overall project and will gain the necessary knowledge about both the V2X vertical application domain and mobile telecommunication technology.
How do you want to contribute to tomorrow's world? How big can your impact be? Come and work at TNO and envision it.
What we expect from you
- You are a graduate student pursuing a Master's degree, preferably in the direction of Electrical Engineering or Computer Science.
- You have affinity with / interest in mobile networks, computer simulations and programming experience.
- You have an enterprising, flexible and cooperative nature.
- You are also communicative, creative and innovative.
- Duration of the graduation project is about nine months.
What you’ll get in return
You want to work on the precursor of your career; a work placement gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It’s not just looking that interests us; you and your knowledge are essential to our innovation. That’s why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your work placement and be given the scope for you to get the best out of yourself. Naturally, we provide suitable work placement compensation.
TNO as an employer
At TNO, we innovate for a healthier, safer and more sustainable life. And for a strong economy. Since 1932, we have been making knowledge and technology available for the common good. We find each other in wonder and ingenuity. We are driven to push boundaries. There is all the space and support for your talent and ambition. You work with people who will challenge you: who inspire you and want to learn from you. Our state-of-the-art facilities are there to realize your vision. What you do at TNO matters: impact makes the difference. Because with every innovation you contribute to tomorrow’s world. Read more about TNO as an employer
The selection process
After the first CV selection, the application process will be conducted by the concerning department. TNO will provide a suitable internship agreement. If you have any questions about this vacancy, you can contact the contact person mentioned below.
Due to Covid-19 and the consequent uncertainties and restrictions, students who are not residing in the Netherlands may currently not be able to start an internship or graduation project at TNO.
Has this job opening sparked your interest?
Then we’d like to hear from you! Please contact us for more information about the job or the selection process. To apply, please upload your CV and covering letter using the ‘apply now’ button.
Contact: Haibin Zhang
Note that applications via email and third party applications are not taken into consideration.
Phone number: +31 (0)88-86 67373