Master's Thesis: AI-Driven Heterogeneous Resource Scheduling for 5G/6G Baseband Processing
publicerat av Huawei Sweden R&D
- Företag
- Huawei Sweden R&D
- Ort
- Kista
- Sista ansökningsdag
- Ej angiven
- Nivå
- Master/civilingenjör (30 hp)
- Period
- Start in spring 2027.
- Passar program
- Computer Science, Electrical Engineering, Information and Communication Technology, Machine Learning
Beskrivning
Advanced 5G/6G networks place demanding computational requirements on baseband processing, including massive MIMO beamforming, channel estimation and decoding. Static and heuristic scheduling can struggle with changing traffic and channel conditions, leading to inefficient hardware use, latency violations and excessive power consumption. This thesis at Huawei Sweden R&D explores AI-driven scheduling of heterogeneous computing resources to improve utilization while meeting strict latency and power limits.
The assignment
- Investigate intelligent, dynamic mapping of baseband processing workloads onto heterogeneous accelerators.
- Explore how scheduling can adapt to varying traffic and channel conditions within sub-millisecond timing requirements.
- Address hardware utilization, processing deadlines and power constraints in the scheduling approach.
Who we are looking for
- Master’s student with a background in deep learning, reinforcement learning, machine learning frameworks such as TensorFlow or PyTorch, and AI model optimization.
- Strong written and spoken English.
- Able to work independently and collaborate effectively with others.
- Careful approach to detail and a focus on high-quality results.
- Experience in wireless communications, signal processing or 5G/NR is an advantage.
What we offer
- An opportunity to apply AI expertise to practical challenges in next-generation telecommunications.
- An innovation-focused R&D environment with international engineering collaboration.
Practical details
Location: Kista. Planned start: spring 2027.
Förkunskapskrav
Applicants should be master’s students with knowledge of deep learning, reinforcement learning, machine learning frameworks such as TensorFlow or PyTorch, and AI model optimization. Strong English communication skills, attention to detail, and the ability to work independently and in teams are required. Wireless communications, signal processing and 5G/NR experience are desirable.
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