Wireless Networks and IoE

Propuesta de tesis


Grupo de investigación

RFID-based ubiquitous sensing

Automated ubiquitous sensing will represent a major change in current societal challenges in terms of efficiency (i.e. automatization), sustainability (better models and decision-making), and social wellbeing (security and improved job market). Automated ubiquitous sensing would, for instance, detect humidity or thin water ponds on a road avoiding possible car accidents in a smart city context, improve recycling processes thanks to automated items classification, or predict dehydration of a person in an ambient assisted living environment.

Current solutions for automated sensing, including active and passive sensors, use specific circuitry for sensing besides wireless communications technologies to transmit the measurements. This approach implies battery management complexity and/or expensive customized technology, being inappropriate for actual ubiquitous sensing.

This proposal aims to exploit passive low-cost communication technologies like Ultra High Frequency (UHF) Radio Frequency Identification (RFID) to act as ubiquitous sensors, by means of analyzing context effects on the RFID labels performance (i.e. see [1,2,3]). The goal is to perform the first steps towards a complete environment digitization thanks to actual ubiquitous sensing.


[1] Melià-Seguí and Pous (2014): http://dx.doi.org/10.1109/IOT.2014.7030112

[2] Hasan, Bhattacharyya and Sarma (2015): https://doi.org/10.1109/RFID-TA.2015.7379812

[3] Melià-Seguí and Vilajosana (2019): https://doi.org/10.1109/RFID.2019.8719092

Dr Joan Melià

Dr Xavi Vilajosana


Context-aware applications for ambient intelligence

Smart cities are a new scenario where a variety of data sources open the door to innovative applications, with the final goal of improving the quality of life of citizens, industry competitiveness and government. The Internet of everything is responsible for collecting and transmitting this data at different levels, by using a wide variety of sensors (including peoples' smartphones). We propose to make use of contextual intelligence tools and techniques (like data mining or statistical learning) to process signals (accelerometers, acoustic, etc.) with the overall goal of transforming the above collected data into useful information, enabling new applications or improving existing ones within the ambient intelligence context.

Dr Joan Melià

Dr Carlos Monzo


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.

Dr Xavi Vilajosana


Dr Ferran Adelantado


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

Dr Ferran Adelantado WINE

Wireless network optimization via experience share

Wireless networks are facing an exponential increase in traffic demand along with a massive increase in density of devices and heterogeneity. Given such complexities, traditional optimization frameworks based on analytical characterization of the network behaviour are expected to be intractable or provide answers difficult to understand and implement in practice. In contrast, learning techniques (such as reinforcement learning) can inherently capture the resulting complex interactions among devices and provide optimality by learning from experience. In this thesis, the use of reinforcement learning mechanisms will be studied in order to overcome the limitations of analytical characterization with the goal of achieving efficient and fair spectrum usage in complex wireless systems, such as 5G networks and in the IoT landscape. The main challenges in applying reinforcement learning to wireless networking optimization are: a) to reduce complexity of network abstractions, b) to amortize the cost of exploratory tasks used to obtain new knowledge, and c) to reduce convergence times. This thesis will explore the use of experience share among multiple network devices to address these challenges and make reinforcement learning applicable to wireless network optimization.

Dr Cristina Cano

Dr Xavi Vilajosana


Sustainable computing

This research proposals aims at taming the energy consumption of computing services in the future Internet. This is a pressing need for a greener society, considering that the worldwide energy consumption of computing facilities is expected to rise to 1/4 th of the world´s electrical energy by 2030. Emerging applications, such as extended reality, smart health, smart factories and autonomous driving, to name a few, require massive amounts of computation resources to process huge volumes of data that will be generated by Internet of things (IoT) devices.  Multi-access edge computing (MEC) technology provides such computation services right at the network edge and enables important opportunities to impose stricter and greener computing policies. This proposal will target the edge computing space with the goal of cutting down their carbon footprint, adopting novel green design principles and techniques, and taking advantage of energy harvesting technologies to be installed at the network edge. 


[1] Patent: US2016247085 (A1) Managing computational workloads of computing apparatuses powered by renewable resources.  MICROSOFT TECHNOLOGY LICENSING LLC 02/05/2016

[2] K. Zhang, S. Leng, Y. He, S. Maharjan and Y. Zhang, "Mobile Edge Computing and Networking for Green and Low-Latency Internet of Things," in IEEE Communications Magazine, vol. 56, no. 5, pp. 39-45, May 2018.

Dr Xavier Vilajosana WiNE