Master Thesis: Brain-Inspired Algorithms for Radio Access Networks

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About this opportunity:

Future Radio Access Networks (RANs) will need to support increasingly demanding workloads under stringent requirements for latency, reliability, scalability, adaptability, and computational efficiency. These workloads operate on dynamic, noisy, and heterogeneous data, including time-varying radio signals, changing channel conditions, user mobility, traffic fluctuations, and evolving network configurations.

This thesis will investigate brain-inspired algorithms in the context of representative RAN workloads. The objective is to evaluate the trade-offs among task performance, latency, robustness, adaptability, computational complexity, scalability, and hardware suitability.

The work is expected to start in January or February 2024 and is proposed for two students for a duration of six months. The location is Ericsson Research in Stockholm (Kista), Sweden. Please submit your application in English as soon as possible, as candidate selection is ongoing.

What you will do:

• Review brain-inspired algorithms for RAN workloads.

• Select a representative RAN use case, research question, and evaluation metrics, focusing on a promising set of capabilities.

• Prepare data and experimental scenarios suitable for the selected capabilities and RAN workload.

• Implement appropriate conventional baseline methods and obtain reference KPIs.

• Develop and validate a brain-inspired algorithm, with primary emphasis on a selected capability such as robust inference, low-latency processing, multi-timescale temporal processing, sparse representations, or adaptation.

• Evaluate the proposed algorithm against the selected baselines using the defined evaluation metrics.

• Assess hardware suitability by analysing operation count, memory requirements, sparsity, parallelism, and related factors.

• Document the solution and evaluation results.

The skills you bring:

• You are pursuing a Master's degree in Machine Learning, Mathematics, Engineering Physics, Computer Science, Embedded Systems, or a related field.

• You have a strong foundation in probability theory, deep neural networks, spiking neural networks, or coupled dynamical systems.

• You have an understanding of neuromorphic computing hardware, such as Loihi 2 or SpiNNaker 2, and software simulation frameworks.

• You have good programming skills and knowledge of C++, Python, and Linux.

• You have strong analytical and problem-solving skills.

• You have good technical writing and communication skills.

The following knowledge or experience is considered a plus:

• Experience with brain-inspired algorithms or neuromorphic computing.

• Familiarity with RAN workloads, wireless communication systems, or signal processing.

• Experience with experimental evaluation, KPI analysis, or hardware-oriented modelling.

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: 791038