In the past decades, the vision had been to provide wireless connectivity to " ... anything that may benefit from being connected...". As a result, there are now an exponentially growing number of wirelessly connected devices, ranging from small sensors in industry/ home and businesses to cars and other applications. When such a large number of devices communicate in an ultra-dense environment, interference became the key challenge for radio resource management or limits the ultimate performance of the wireless network. We can infer that the estimation of interference needs to be carried out proactively. For this purpose, we will introduce machine learning(ML) for robust and proactive interference estimation. 

Strict Requirement:

• Either strong Analytical Skills or hands-on experience with ML 

Feel free to write me an email if you are interested.

Suggested Literature:

[1]. G. Berardinelli et al., "Extreme Communication in 6G: Vision and Challenges for ‘in-X’ Subnetworks," in IEEE Open Journal of the Communications Society, vol. 2, pp. 2516-2535, 2021, doi: 10.1109/OJCOMS.2021.3121530.