Master thesis: Semantic Communications with Foundation Models for Vehicle and Road User Detection

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About this opportunity:

Next-generation wireless networks, including 5G-Advanced and 6G, will connect large numbers of bandwidth-constrained devices such as vehicles, surveillance cameras, and drones. Transmitting raw or conventionally compressed images can use valuable spectrum on details irrelevant to a particular task. Semantic communication instead aims to transmit task-relevant information-such as detected objects and their locations-while preserving what is needed for downstream decisions.

Vision foundation models such as CLIP and DINOv2 can provide rich image representations without task-specific pretraining. This thesis will investigate whether their spatial features can be compressed and transmitted over a noisy wireless channel while maintaining object-detection performance.
Research Question
How well do frozen foundation-model spatial features withstand compression and wireless-channel noise when used to detect vehicles and other road users?

What you will do:

Build and evaluate a semantic-communication system that extracts spatial features from images using a frozen CLIP-ViT-B/16 or DINOv2-ViT-B/14 model, compresses them through a learned linear bottleneck, quantizes and transmits them over a simulated additive white Gaussian noise (AWGN) channel, and reconstructs bounding boxes and class labels with a lightweight decoder. The study will use six driving-relevant COCO classes: car, truck, bus, motorcycle, person, and bicycle.

• Review semantic communications, joint source-channel coding (DeepJSCC), foundation models, and JPEG compression.
• Extract patch-level features from the selected COCO classes.
• Implement the learned bottleneck, quantization, AWGN channel simulation, and lightweight anchor-free detection decoder.
• Train the bottleneck and decoder end-to-end with noise injection at random signal-to-noise ratio (SNR) levels.
• Compare both foundation-model approaches with a task-specific encoder-decoder and standard baselines such as JPEG.
• Analyze results in a communications context, including mean average precision (mAP) versus SNR and per-class performance.

The skills you bring:

• You are pursuing a master's degree in Electrical Engineering, Computer Science, Telecommunications, or a related field.
• Strong programming skills in Python and PyTorch.
• Good understanding of deep-learning concepts, including CNNs, transformers, training procedures, and loss functions.
• Ability to work independently, design experiments, and critically interpret results.
• Fluency in written and spoken English.
• Familiarity with object detection (e.g., YOLO, Faster R-CNN, or FCOS), wireless communications (e.g., SNR and AWGN), or pretrained vision models (e.g., CLIP, ViT, or DINOv2) is an advantage.

Project Details and Contacts

• Extent: One student
• Contact: Aneta Vulgarakis
• Supervisors: Cristian Vaca Rubio, Maxim Teslenko

Why join Ericsson?At Ericsson, you'll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what's possible. To build solutions never seen before to some of the world's toughest problems. You'll be challenged, but you won't be alone. You'll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.

What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more.

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

Req ID: 789652