PhD Topic 

With the current energy crisis, the skyrocketing cost of energy, and the global awareness of the consequences of carbon emissions on our planet, it is crucial to reduce the global energy consumption related to Information and Communication Technologies, and particularly the one related to networks.

Indeed, while being commercialized worldwide, the 5G technology coupled with ultra-high resolution video has been blamed for its high energy consumption [9]. To answer this issue, industrials, and researchers have started to look beyond 5G to define the next 6G with at its core the need to evolve towards greener networks. However, at the same time, 6G will introduce several technological breakthroughs, particularly the utilization of Artificial Intelligence (AI) techniques to provide context-aware information transmissions and personal customized services, as well as to realize automatic network management. 

Thus, succeeding in the challenge of developing more energy efficient networks will require significant improvement in several directions: e.g. relaunching measurement campaigns, rethinking protocols, developing energy efficient network management algorithms, adopting energy harvesting techniques, deciding if and when data should be shared and transfer.

Recently, generative AIs (Gen AIs) have been demonstrated playing a key role in data sharing while preserving privacy and security . Generative AI models generate synthetic data which distribution is similar to original data one. So, instead of sending original data, many applications use Gen AIs to transfer data to preserve privacy, security and resource.  However, the highly distributed architecture in 6G motivates the need for distributed, multi-agent learning for building generative models located at given anchor points of data collection inside the RAN/Edge. Thus, for applications requiring AI-based generative services locally located at data collection points, it is necessary to deploy distributed AI-based generative services that work together independently of their location. 

As a result, the number and the size of such services has to handle increases drastically with the multiplication of nodes in 6G and the diversification of AI-based generative services addressed by 6G such as different industries (telecom, digital twins, manufacturing, and healthcare). At the same time, with the consideration of micro-services composed to build virtualized network functions which are then assembled to form network slices, the size of the latter also increases. Consequently, new methods to provision/manage them on the fly in a fast and scalable way must be considered, while not increasing network energy consumption. 

In this thesis, we will focus on the provisioning and management of energy-efficient slices and generative AI services. Throughout the PhD program, we will address these issues in accordance with the following guidelines:

1. First, we plan to refine existing power models as they are the foundations to discuss energy efficiency. We will start from methods proposed to estimate the carbon footprint of a typical streaming service. We will consider applications such as digital twin and smart factory.
2. Secondly, we intend to delve into the application of novel AI methods specifically aimed at addressing the challenge of energy reduction. We will thus investigate how to use new AI methods to provide approximate solutions for provisioning a very large number of network services while minimizing the associated energy consumption.
3. Third, we plan to study how AI methods can help to efficiently reconfigure the networks when (i) demand or (ii) energy availability has changed while considering of the life cycle of GenAI services.  

The challenge is to find ways to manage the networks and the expected demands with such varying sources of energy. We will study how AI models using energy predictions can allow reconfiguring the network in advance to adapt to planned changes in power, e.g., to choose when doing a large backup, to plan the switching off of some network parts.
 

N/A

Ideal candidate should have a solid Mathematical background, especially in probability and statistics. The candidate should be eager to tackle new research challenges in the area of generative models, federated learning, energy-efficient management, in deep learning and distributed optimizations. He/she should have a good background in networking technologies and familiar with 5G. It is imperative that the candidate has a perfect proficiency in Bash/Shell script, Python, C/C++ and Tensorflow, Keras, Pytorch…, or other scripting languages. Lastly, the candidate should demonstrate a good autonomy and motivation. Good communication skills (written and verbal) in both English and French 

Knowledge in 6G shall be a plus. 

 

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