ARISTIDES - Aprendizaje Estadístico e Inferencia para Sistemas de Comunicación de Alta Dimensionalidad

Ico_CTTC
Start: 01/01/2019
End: 31/08/2022
Funding: National
Status: On going
Research unit:
Information and signal processing for intelligent communications (ISPIC)
Acronym: ARISTIDES
Call ID: RTI2018-099722-B-100
Code: RTI2018-099722-B-100

The ARISTIDES project aims to deepen the theoretical understanding and advance on the performance of data-driven learning and inference algorithms for high-dimensional data processing. A special focus is set on the enhancement of machine learning methods (incl. deep learning) and their application to the re-design of lower-layer functionalities of (beyond) 5G communication systems.

The algorithmic tools investigated include (i) random matrix theory to better model the behavior of kernel-based learning algorithms; (ii) structured sparsity methods to reduce the dimensionality of complex learning problems with a large number of features; (iii) Bayesian inference to enrich learning algorithms with any prior information on the underlying model; and (iv) coded computing strategies to speed up the execution of learning algorithms executed in a distributed manner.

The project will study the applicability of these methods, in combination with other well-known data-driven approaches (e.g., deep learning and reinforcement learning) to the design and optimization of key functionalities in communication systems, with particular emphasis on the PHY layer. Specifically, the ARISTIDES project will (i) investigate and develop data-driven beam/antenna selection and user clustering schemes in mmWave communications for improved performance–complexity trade-off, robustness and scalability; (ii) devise end-to-end learning, autoencoder-inspired techniques for efficient code design for ultrareliable low-latency communication (URLLC); (iii) analyze the feasibility of deep neural network and reinforcement learning-based designs for massive PHY/MAC access schemes; (iv) build a proof-of-concept based on software radio for an end-to-end trained short-packet communication system.

Carles Antón-Haro
PI/Project Leader
Ana Moragrega
Researcher
Adriano Pastore
Researcher
Carles Fernández-Prades
Researcher
Javier Arribas
Researcher
Xavier Mestre
Researcher
Marc Majoral
Researcher
Jesús Gómez-Vilardebó
Researcher
Monica Navarro
Researcher
Armin Ghani
Researcher
Dheeraj RajaKumar
Researcher
Adrián Agustin
Researcher
Centre Tecnològic de Telecomunicacions de Catalunya
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