Projektbeschreibung
Ein neues Modell für Kommunikationsnetzwerke
Im Verlauf der Geschichte der Kommunikation beruht kritische Infrastruktur schon seit Langem auf den Grundlagen, die von den Telefonnetzwerken Mitte des 20. Jahrhunderts geschaffen wurden. Allerdings sind die Anforderungen moderner Entwicklungen und Anwendungen an die Datenübertragungsrate so sehr gewachsen, dass die herkömmliche Infrastruktur nicht mehr die nötige energieeffiziente, latenzarme und schnelle Kommunikationsgrundlage bereitstellen kann. Das EU-finanzierte Projekt RENEW zielt darauf ab, die Datenübertragungsraten und die Effizienz moderner Kommunikationsnetzwerke zu verbessern und gleichzeitig ihre Auswirkung auf die Umwelt einzuschränken. Zu diesem Zweck wird es hochmoderne Technologien einsetzen, um die Komplexität und den Energieverbrauch bestehender Systeme drastisch zu senken und so auf der ganzen Welt einer fortgeschrittenen, nachhaltigen Kommunikation den Weg zu ebnen.
Ziel
To this day, communications engineering has closely followed the seminal guidelines developed by Claude E. Shannon in 1948, which were mostly influenced by the telephone network of those days. The widespread use of mobile communications and the advent of machine-to-machine communications nowadays entail an exponential increase in data rates and the available models are no longer sufficient to design power-efficient, low-latency, high-speed communication systems. The overarching aim of RENEW is to further increase the data rates of the global telecommunication network while, at the same time, addressing its non-negligible environmental impact. By fundamentally revisiting the transceiver processing algorithms of the core parts of the communication network, RENEW has the potential to overcome the limitations of current design methodologies and to significantly reduce the complexity and energy consumption of the network. Capitalising on cutting-edge results in the fields of machine learning, reinforcement learning, optimisation techniques and neuromorphic computing, RENEW will reinvent the design of communication transmitters and receivers by introducing sparsely connected atomic neural blocks that realise highly parallelisable transceivers guaranteeing high throughputs with low energy consumption. RENEW will explore novel concepts for extremely energy efficient receivers based on spiking neural networks, promising efficiency gains by multiple orders of magnitude. The viability of the RENEW concepts will be demonstrated in applications of high relevance such as high-speed optical communication networks or low-power IoT applications. My industrial experience designing high-speed optical communications, together with my background in coding and communication theory as well as machine learning techniques will be an important enabler for the RENEW concept, which has a transformative potential as it will consequently yield novel energy efficient communication systems.
Wissenschaftliches Gebiet
Not validated
Not validated
- natural sciencescomputer and information sciencesinternetinternet of things
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunications
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programm/Programme
Thema/Themen
Finanzierungsplan
ERC-COG - Consolidator GrantGastgebende Einrichtung
76131 Karlsruhe
Deutschland