Descrizione del progetto
Una nuova pista per l’intelligenza artificiale wireless
I sistemi di comunicazione wireless devono affrontare sfide sempre più impegnative, poiché devono gestire un numero maggiore di dati in ambienti dinamici. I metodi tradizionali di progettazione dei ricevitori faticano a tenere il passo con i rapidi cambiamenti dei canali wireless. Inoltre, i dispositivi dispongono di potenza e risorse di calcolo limitate, che rendono difficile l’elaborazione di grandi quantità di dati. Le attuali soluzioni di IA, che si basano su reti massicce pre-addestrate, non sono adatte a queste condizioni. In questo contesto, il progetto FLAIR, finanziato dal CER, intende affrontare questi problemi creando una nuova forma flessibile di IA progettata specificamente per le comunicazioni wireless. Si concentra su progetti di ricevitori leggeri, apprendimento continuo e uso efficiente dei dati, offrendo un approccio più adattabile e rispettoso delle risorse.
Obiettivo
Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in challenging communication scenarios. However, wireless receiver design poses unique challenges that fundamentally differ from those encountered in traditional deep learning domains. The main challenges arise from the dynamic nature of wireless communications, which causes continual changes to the data distribution, combined with the limited power and computational resources of wireless devices. These challenges impair conventional AI based on offline trained massive DNNs. Our ambitious goal is to introduce a new form of flexible lightweight AI that is particularly tailored for wireless communications. Our approach is based on a holistically revisiting the three fundamental pillars of AI – the architecture, dictating the family of learned mappings; the training algorithm that tunes the architecture; and the data based on which learning is carried out. Accordingly, we focus on three objectives – 1) design trainable receiver architectures that are lightweight and support adaptation to rapid channel variations; 2) establish a new learning paradigm that deviates from conventional training, and is based on viewing continual learning as a dynamic system; and 3) propose techniques to accumulate online data sets that are sufficiently informative for learning purposes while being small enough not to induce notable complexity in training. This is a fundamental depart from conventional deep learning, based on highly-parameterized DNNs trained with massive data sets using lengthy learning procedures. Our preliminary data show that this paradigm shift achieves substantial performance, robustness, and complexity gains over conventional deep receivers. The project will transform how communications systems are studied, and profoundly impact a multitude of applications that rely on wireless communications.
Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
84105 Beer Sheva
Israele