Machine learning (ML) methods are expected to have an important role in optimizing the 6G network design and operation from the energy efficiency perspective. Brute force ML methods when applied as black box neural network require huge computation complexity in terms of its financial cost, implementation complexity and processing power consumption. 6GLearn Project aims at systematic energy-efficient 6G design and optimization using the mix of analytical and ML tools with physical channels. The capitalization of the physical radio channels and true hardware devices is the core to achieve computational simplicity and holistic sustainable energy efficient 6G joint communications and sensing network operation.
|Effective start/end date||1/01/23 → 31/12/25|
- VTT Technical Research Centre of Finland
- University of Oulu (lead)
- 6G Bridge
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