Project title: «Deep Intelligent Optical and Radio Communication Networks»
Type of Action: MSCA-RISE-2020 – Research and Innovation Staff Exchange
Grant Agreement #101008280 (DIOR)
Project duration is 48 months (2021 – 2024)
Coordinator: TAMPERE UNIVERSITY, FI (https://www.tuni.fi/fi)
Consortium:
THE UNIVERSITY OF WARWICK, UK (https://warwick.ac.uk/)
SKEIN LTD, UA (https://skein.co/)
INSTITUTO DE TELECOMUNICACOES – IT, PT (https://www.it.pt/)
TURING INTELLIGENCE TECHNOLOGY LIMITED, UK (https://turintech.ai/)
KHARKIV NATIONAL UNIVERSITY OF RADIO ELECTRONICS, UA (https://nure.ua/)
FUDAN UNIVERSITY, CN (https://www.fudan.edu.cn/)
TIANJIN UNIVERSITY, CN (http://www.tju.edu.cn/)
Project objectives:
- Processing the signal and network traffic optimization from analytical approaches to data-driven inferring, especially neural network (NN) based approaches.
- Developing the convolutional and recurrent NN models in optical fiber and radio communication networks to mitigate stochastic distortions, to optimise network lightpath and to facilitate network load allocation. Exploiting heterogeneous data analysis to build models for optical fibre and radio access channels and to estimate traffic load to optimise both the deployment and operations of optical and radio networks.
- Designing Machine Learning (ML) algorithms and Artificial Neural Networks (ANN) that facilitate future high-capacity optical and radio communications to enable a new age of intelligent communication networks beyond 5G.
Expected results:
- To develop new ML-based digital pre-distortion (DPD) methods for linearizing base-station power amplifiers.
- To develop fusion model for acoustic/video context information (user behaviour,
trajectory, location) and radio beamforming design for high accurate beam alignment & management in 5G NR.
- To develop the ANN-based baseband radio processing without dependence of
explicit channel model.
- To curate high-resolution physical layer and traffic demand data to achieve
high-fidelity channel models.
- To develop the AI-based optical channel model for optical fiber system to include
stochastic impairments.
- To develop ANN frameworks to compensate for stochastic nonlinear impairments in optical fibers and semiconductor lasers.
- To design algorithms and signal path topologies to optimally allocate the latency and
load of communication networks with intelligent flexibility.
- To design mechanisms to integrate data and load analysis to optimise the resource allocation of optical and radio access networks.
Project Summary on CORDIS – EU research results https://cordis.europa.
Project website https://dior-rise.eu/