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.
Fields of science
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunications
Call for proposal
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