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ML for energy efficient 6G networks

   School of Physics, Engineering and Technology

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  Dr H Ahmadi  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Nowadays, researchers have started to conceptualize 6G with the vision of connecting everything, transmission over mmWave and THz, and integrating sensing, communication, computation, and control functionalities. To support such network evolution, the deployment of small and even tiny cells is further densified overlaying with the existing macro cellular networks. The resultant technical and network complexity puts considerable pressure on energy efficiency and sustainability.

Artificial intelligence (AI) and machine learning techniques have great potential to tackle the energy efficiency challenges in the future green 6G. AI methodologies, e.g., deep learning, federated learning and reinforcement learning, can be explored for the design and optimization of 6G architecture and network orchestration in a cost-efficient manner. By learning the complex network topology and the varying traffic pattern, AI could tame network complexity for the design of 6G air interfaces. The diversified 6G enabling applications, such as smart cities, smart grid, autonomous vehicles, and industrial automation, will make AI more far-reaching and essential in energy savings. On the other hand, AI and machine learning techniques usually demand high computation and communication. This may pose a significant challenge for the design and implementation of both machine learning algorithms and future 6G systems in an energy-efficient way. One advantage is that 6G's Gb-level transmission rate will possibly bring a radical paradigm shift for AI toward ubiquitous AI, taking advantage of distributed machine learning and edge intelligence.

Entry requirements:

Candidates should have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in Electronic and Electrical Engineering, Physics, Computer Science, Mathematics or a closely related subject.

How to apply:

Applicants should apply via the University’s online application system at Please read the application guidance first so that you understand the various steps in the application process.

Funding Notes

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website for details about funding opportunities at York.
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