Explore the words cloud of the AGNOSTIC project. It provides you a very rough idea of what is the project "AGNOSTIC" about.
The following table provides information about the project.
UNIVERSITE DU LUXEMBOURG
|Coordinator Country||Luxembourg [LU]|
|Total cost||2˙499˙595 €|
|EC max contribution||2˙499˙595 € (100%)|
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
|Duration (year-month-day)||from 2017-10-01 to 2022-09-30|
Take a look of project's partnership.
|1||UNIVERSITE DU LUXEMBOURG||LU (ESCH-SUR-ALZETTE)||coordinator||1˙843˙447.00|
|2||KUNGLIGA TEKNISKA HOEGSKOLAN||SE (STOCKHOLM)||participant||656˙148.00|
Parameterized mathematical models have been central to the understanding and design of communication, networking, and radar systems. However, they often lack the ability to model intricate interactions innate in complex systems. On the other hand, data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of flexibility. These approaches need labelled data, representing all the facets of the system interaction with the environment. With the aforementioned systems becoming increasingly complex with intricate interactions and operating in dynamic environments, the number of system configurations can be rather large leading to paucity of labelled data. Thus there are emerging networks of systems of critical importance whose cognition is not effectively covered by traditional approaches. AGNOSTIC uses the process of exploration through system probing and exploitation of observed data in an iterative manner drawing upon traditional model-based approaches and data-driven discriminative learning to enhance functionality, performance, and robustness through the notion of active cognition. AGNOSTIC clearly departs from a passive assimilation of data and aims to formalize the exploitation/exploration framework in dynamic environments. The development of this framework in three applications areas is central to AGNOSTIC. The project aims to provide active cognition in radar to learn the environment and other active systems to ensure situational awareness and coexistence; to apply active probing in radio access networks to infer network behaviour towards spectrum sharing and self-configuration; and to learn and adapt to user demand for content distribution in caching networks, drastically improving network efficiency. Although these cognitive systems interact with the environment in very different ways, sufficient abstraction allows cross-fertilization of insights and approaches motivating their joint treatment.
|year||authors and title||journal||last update|
Yang Yang, Marius Pesavento, Symeon Chatzinotas, Bjorn Ottersten
Successive Convex Approximation Algorithms for Sparse Signal Estimation With Nonconvex Regularizations
published pages: 1286-1302, ISSN: 1932-4553, DOI: 10.1109/JSTSP.2018.2877584
|IEEE Journal of Selected Topics in Signal Processing 12/6||2019-07-24|
Satyanarayana Vuppala, Thang X. Vu, Sumit Gautam, Symeon Chatzinotas, Bjorn Ottersten
Cache-Aided Millimeter Wave Ad Hoc Networks With Contention-Based Content Delivery
published pages: 3540-3554, ISSN: 0090-6778, DOI: 10.1109/tcomm.2018.2820694
|IEEE Transactions on Communications 66/8||2019-07-24|
Anestis Tsakmalis, Symeon Chatzinotas, Bjorn Ottersten
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks
published pages: 6-19, ISSN: 1932-4553, DOI: 10.1109/JSTSP.2017.2785826
|IEEE Journal of Selected Topics in Signal Processing 12/1||2019-07-24|
Marie Maros, Joakim Jalden
On the Q-linear convergence of Distributed Generalized ADMM under non-strongly convex function components
published pages: 1-1, ISSN: 2373-776X, DOI: 10.1109/tsipn.2019.2892055
|IEEE Transactions on Signal and Information Processing over Networks||2019-07-24|
Ahmad Gharanjik, M. R. Bhavani Shankar, Frank Zimmer, Bjorn Ottersten
Centralized Rainfall Estimation Using Carrier to Noise of Satellite Communication Links
published pages: 1065-1073, ISSN: 0733-8716, DOI: 10.1109/jsac.2018.2832798
|IEEE Journal on Selected Areas in Communications 36/5||2019-07-24|
Ying Cui, Zitian Wang, Yang Yang, Feng Yang, Lianghui Ding, Liang Qian
Joint and Competitive Caching Designs in Large-Scale Multi-Tier Wireless Multicasting Networks
published pages: 3108-3121, ISSN: 0090-6778, DOI: 10.1109/tcomm.2018.2807445
|IEEE Transactions on Communications 66/7||2019-07-24|
Zheng Chang, Lei Lei, Zhenyu Zhou, Shiwen Mao, Tapani Ristaniemi
Learn to Cache: Machine Learning for Network Edge Caching in the Big Data Era
published pages: 28-35, ISSN: 1536-1284, DOI: 10.1109/mwc.2018.1700317
|IEEE Wireless Communications 25/3||2019-07-24|
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