Master Thesis Internship AI Research: AI based cell shaping optimization in 6G - Massy, France, Paid Internship

Master Thesis Internship AI Research: AI based cell shaping optimization in 6G, Massy, France

Req: 751364

Paid Internship 

Why join Ericsson?

We are a world leader in the rapidly changing environment of communications technology – by providing hardware, software, and services to enable the full value of connectivity.

You’ll play a part in using your skills and creativity to push the boundaries of what’s possible. To build never-seen-before solutions to some of the world’s toughest problems.

Join a team of like-minded innovators driven to go beyond the status quo to build what’s next.

At Ericsson, you can be an explorer, a change maker and a force for good.

Our purpose: To create connections that make the unimaginable possible.

Our vision: A world where limitless connectivity improves lives, redefines business and pioneers a sustainable future.

Our values: Our culture is built on over a century of courageous decisions, in a place where co-creation and collaboration are embedded in the walls and where our core values of professionalism, respect, perseverance and integrity shine through in everything we do.

About this opportunity 

The exponential increase of mobile traffic due to video and virtual reality applications results in high energy consumption. To make 5G and future 6G services sustainable, it is essential to reduce the telecom network carbon footprint. AI-driven management of radio RAN resources guided by intents, i.e. Service Level Agreement (SLA) between an operator and a service provider, can ensure the performance while minimizing the energy consumption. High mmWave (millimeter wave) frequencies are pivotal for advancing 5G and 6G networks, offering unparalleled data rates and network capacity. Operating between 30 GHz and 300 GHz, the mmWave spectrum is essential to meet the ever-growing demand for wireless data, enabling multi-gigabit speeds and ultra-low latency. This technological capability supports a wide range of innovative applications such as high-definition video streaming, augmented reality (AR), virtual reality (VR), and real-time interactive gaming. Despite these advantages, mmWave technology presents unique challenges due to its shorter wavelengths, which result in limited propagation range and increased vulnerability to physical obstacles like buildings and foliage. Effective cell shaping becomes indispensable in optimizing mmWave deployments. By dynamically adjusting cell coverage areas and beamforming patterns, cell shaping mitigates these challenges and enhances overall network performance. The Synchronization Signal Block (SSB) codebook is central to this optimization.

In this Master Internship, we will focus on utilizing AI, and in particular Reinforcement Learning (RL), to optimize Synchronization Signal Block (SSB) codebook selection in multi-cell mmWave 5G networks. This research aims to harness AI’s capabilities to dynamically optimize codebook selection, unlocking the full potential of mm-wave technology in modern wireless communications. The project addresses two primary deployment scenarios in high mm-wave environments: coverage-limited scenarios, where high bands complement low bands to manage traffic, and interference-limited scenarios, where high-band cells may interfere with each other.

Join our Team

You’ll get the opportunity to investigate different solution using Multi-agent Reinforcement Learning (RL) for automating the selection of SSB codebooks in high mmWave environments to enhance the performance of future mobile networks. You will familiarize yourself with the simulator and customized Gym SSB-environment, review existing literature and frameworks on Multi-agent RL. You will then propose novel Multi-agent RL techniques and adapt the environments to incorporate service differentiation among UEs, and finally, evaluate algorithm performance through simulations or experiments in realistic scenarios.

Key skills

To apply for this role, you are in your second year of Master or the final year of engineering school and be studying a degree in the following subjects (or related): Computer Science, AL/Machine Learning, Electrical Engineering, Mathematics, or Physics.  

To be successful in the role you have:

Good understanding of Machine Learning algorithms (Reinforcement Learning, Generative-AI, Neural Networks, times series, etc.).
Good foundation in practical programming skills, experience in Python and in using scientific Python packages such as PyTorch, Scikit-learn, Numpy, etc.
Ease with reading and implementing recent machine learning research articles from the established conferences (ICLR, ICML, NeurIPS etc).
Background in telecommunication networks will be appreciated, although not a must. Rather a genuine interest in the applications of the telecom (5G/6G) world and willingness to learn.
Excellent communication skills both in written and spoken English.
Knowledge about stack technologies and cloud architectures (optional but recommended): Git, Docker, Kubernetes.

Interns who join us will enjoy an outstanding chance to make connections, to make change, to make a real difference. We are always looking for interns to become permanent members of our Ericsson team, so you’ll have the opportunity to discuss your future with us during your internship.