Research

The emerging 6G vision of seamless connectivity between the cyber and physical worlds will shape next-generation networks and services. Intended applications are, e.g., self-driving connected vehicles, health IoT-based services, and critical infrastructure surveillance, which integrate communication and sensing functionalities. Besides, MADDIE foresees that future networks will incorporate devices with quantum information processing capabilities, which may either boost network performance or act as adversaries that jeopardize network security. Therefore, for upcoming services and applications to become a reality, wireless and communication technologies must not only meet the demands of extremely high data throughput and low latency, but also ensure cybersecurity in hybrid classical-quantum networks.

Leveraging MADDIE participants’ know-how and expertise, this project will make use of classical and quantum coding, communications, and signal processing advanced techniques that will play a crucial role in answering the aforementioned challenges. Specifically, MADDIE will design coding and cryptographic algorithms for IoT-based applications in the context of classical-quantum networks. Furthermore, MADDIE will also explore innovative wireless technologies for the operation in the mmWave band and beyond, where future communication networks will be allocated to leverage massive bandwidth availability. Regarding co-existing sensing and communication networks, MADDIE will investigate distributed processing techniques that may possibly be combined with recent advances in graph signal processing to make efficient use of resources and increase performance. Finally, MADDIE will assess the impact of quantum devices in communication networks from both a computational and security perspective.

ADELE mission is twofold. First, it will develop advanced coding and signal processing techniques for wireless communication networks. Furthermore, leveraging on ADELE participants know-how and expertise, the project will address more ambitious long-term objectives and will contribute significantly to: (a) coding and information theory for quantum computing and communications, and (b) machine learning methods and the emerging field of graph signal processing for the analysis and operation of networks that handle massive amounts of data including, but not limited to, wireless communication networks.

Online detection of abrupt changes in time series has received much attention in the past due to its importance in applications such as structural health monitoring or target detection and tracking. In particular, the signal processing community has established the basic theory of change-point detection, which more recently has also attracted the interest of the machine learning community.

Kernel machines and related Bayesian approaches, such as Gaussian processes, have been widely and successfully used in practice for dealing with such data structures for regression and classification. Kernel methods allow to encode prior knowledge about the data characteristics, to learn the underlying latent functions explaining the data; and allow the combination of different data modalities. The long-term vision of KERMES is tied to open new frontiers and foster research towards new kernel algorithms, a stepping stone before the more ambitious far-end goal of machine reasoning.

CARMEN is a three-year (2017-2019) research project sponsored by Gobierno de España (TEC2016-75067-C4-1-R). CARMEN addresses two major trends in current wireless networks: radio interfaces with unprecedented high data rates and Wireless Sensor Networks (WSN).

The COMONSENS Network aims at the fertilization of the research collaboration links initiated and developed during the COMONSENS project both among the 10 participating research groups in Spain and with external participants and networks. The COMONSENS project, www.comonsens.org, involved 135 researchers and surpassed all originally agreed performance objectives. The COMONSENS Network will continue the biannual COMONSENS workshops, which took place between 2009 and 2014.

The goal of PRISMA is to advance the state of the art in machine learning theory and algorithms that exploit temporal information. PRISMA aims to develop a general probabilistic framework to deal with temporal dynamics in time series, based on Bayesian graphical models for temporal pattern recognition. Furthermore, PRISMA aims to extend the state-of-the-art in kernel methods and machine learning algorithms that deal explicitly with the temporal variable in pattern recognition problems. The developed theoretical framework will be evaluated in applications from several key areas of the modern digital society.

Mobile communications market forecasts an exponential growth of mobile subscriptions and data traffic over the next few years. Such trend is driven by a new generation of wireless users equipment, the proliferation of data rate intensive applications and machines willing to communicate over the mobile networks. Conventional approaches to design cellular networks such as increasing the number of macrocell sites, or enhancing the macrocell radio access features, are widely believed to be insufficient to avoid the foreseeable collapse of currently deployed mobile networks.

The objective of COSIMA is the development of novel communication methods at the physical layer level for the interference management in wireless communication networks. Specifically, COSIMA focuses on two paradigms that, undoubtedly, will be key ingredients of future wireless networks: cooperation and cognition. Cooperative strategies are those in which several transmitters and/or receivers coordinate among them to improve efficiency or increase their performance inside a wireless network.

COMONSENS is one of the 12 projects selected in 2008 by CONSOLIDER-INGENIO 2010 Program sponsored by the "Ministerio de Ciencia e Innovación" of the Spanish Government. COMONSENS is a five-year project that integrates 67 researchers from 10 different research institutions in Spain and is coordinated by Universitat Politècnica de Catalunya (UPC).