Ferran Adelantado i Freixer

Associate Professor at UOC

Adjunct Associate Professor at UB

Researcher at Wireless Networks Research Lab (WiNe)



If you are interested in collaborating, please send me an e-mail (ferranadelantado@uoc.edu) and I wil try to reply as soon as possible.

Open Positions

Doctoral Programme in Network and Information Technologies: Call for applications (12 February 2021: Aplication period deadline)

The call for applications is open until 12 February 2021. If you are interested in applying to any of the two thesis proposals described below, please contact ferranadelantado@uoc.edu.

You can find detailed information on how to apply on the website (link).

  • PhD Thesis Proposal 1: Ultra-reliable low latency industrial communication technologies

The digitalization of the industry is a step further to achieve a digital society. Such digitalization will enable more efficient industries with major impact in the quality of work and the generated industrial value and competitiveness. To achieve such digitalization, massive connectivity will be progressively introduced to industrial processes, mainly by extending existing machinery interfaces and integrating to existing industrial infrastructures and information systems on a first stage. This integration of Information Technologies (IT) and Operational Technologies (OT) by itself imposes challenges beyond what is envisioned by the 5G architectures since, industrial processes are not only critical in terms of reliability, latency and security but also in their architecture, heterogeneity and ownership model which will require more flexible network architectures to complement those envisioned by 5G.

In this research proposal we aim to address industrial requirements to support digitalization. The research work will be centered in the study and development of mechanisms to enable ultra-reliable and low latency industrial wireless communications. The envisioned communication technology should support robotics eliminating the need of wires, while ensuring high reliability (99.999%), low latency (<1ms) and secure links. The research work will explore the features provided by novel physical layer technologies, based on mmWave (>30Ghz) bands and exploit redundancy mechanisms to achieve the desired performance.


[1] Yong Niu, Yong Li, Depeng Jin, Li Su, Athanasios V. Vasilakos: A survey of millimeter wave communications (mmWave) for 5G: opportunities and challenges. Wireless Networks 21(8): 2657-2676 (2015)

[2] Loch, A., Cano, C., Hong, G., Asadi, A., & Vilajosana, X. (2019). A Channel Measurement Campaign for mmWave Communication in Industrial Settings. arXiv preprint arXiv:1903.10502.

  • PhD Thesis Proposal 2: Machine/Deep Learning enabled 5G networks

The continuous increase in the mobile data traffic demand has posed significant challenges in the design of 5G networks. Although video traffic has dominated the surge in mobile data traffic demand so far, the growing importance of new services with diverse Quality of Service (QoS) requirements stretches the networks to their capacity. In that sense, three service categories have been identified: enhanced Mobile Broadband (eMBB) services, Ultra-Reliable Low-Latency Communications (URLLC) service and massive Machine-Type Communications (mMTC) services.

The ability to serve such diverse traffic will be determined by the flexibility and adaptability of the network. That is, tailored virtual networks will have to be built on top of the physical network to meet the requirements of each specific service. In this context Machine/Deep learning will play a key role. The exploitation of the data available in future SDN-based networks will be the enabler to efficiently perform network slicing, thus detecting network failures, predicting traffic variations and configuring the network optimally.

Machine/Deep learning has been extensively studied and applied in scientific fields such as Computer Vision. However, its application to communications networks is still in its infancy. This PhD thesis aims to analyse existing Machine Learning and Deep Learning algorithms and evaluate the feasibility and advisability of its application in 5G mobile networks.

We are looking for highly motivated, enthusiastic junior scientists, with an MSc in electrical engineering or related fields, aiming at significantly improving their career. Excellent research skills and analytical abilities are required, fluency in English (spoken and written), proactive communication skills and problem solving as part of a team, strong record keeping, great work ethic and initiatives are essential characteristics.


Candidates must have a strong mathematical background, fluency in English (spoken and written) and excellent analytical and writing skills. Experience in artificial intelligence and networks (especially background in wireless networks will be highly appreciated).

In addition to the requirements stated in the call, all candidates applying for this thesis proposal must send a short report describing: i) Wireless networks background of the candidate; ii) Machine/Deep learning background of the candidate; iii) The research interests of the candidate (within the framework of the proposed thesis). Prior works of the candidates (MSc thesis, BSc thesis, published/submitted papers, code, etc) on wireless networks and Machine/Deep learning will be highly appreciated. This report must be sent directly to ferranadelantado@uoc.edu. Only candidates fulfilling all requirements (general requirements of the call and specific requirements of this thesis proposal) will be considered.

Related references:

[1] R. Li et al., "Intelligent 5G: When Cellular Networks Meet Artificial Intelligence," in IEEE Wireless Communications, vol. 24, no. 5, pp. 175-183, October 2017. doi: 10.1109/MWC.2017.1600304WC

[2] M. Yao, et al., "Artificial Intelligence Defined 5G Radio Access Networks," in IEEE Communications Magazine, vol. 57, no. 3, pp. 14-20, March 2019. doi: 10.1109/MCOM.2019.1800629

[3] Y. Fu, et al., "Artificial Intelligence to Manage Network Traffic of 5G Wireless Networks," in IEEE Network, vol. 32, no. 6, pp. 58-64, November/December 2018. doi: 10.1109/MNET.2018.1800115