Preprint Article Version 1 This version is not peer-reviewed

Considerations on Novel Network Edge Physical Nodes Architectures Leveraging A Bottom-Up Technology-Centric Approach Empowered by Micro/Nanotechnologies (MEMS/NEMS) and Federated Learning

Version 1 : Received: 7 November 2024 / Approved: 8 November 2024 / Online: 8 November 2024 (10:15:04 CET)

How to cite: Iannacci, J.; Bhuyan, M.; Sarma, K. K.; Sharma, P.; Misra, A.; Misra, D. D.; Guha, K. Considerations on Novel Network Edge Physical Nodes Architectures Leveraging A Bottom-Up Technology-Centric Approach Empowered by Micro/Nanotechnologies (MEMS/NEMS) and Federated Learning. Preprints 2024, 2024110603. https://doi.org/10.20944/preprints202411.0603.v1 Iannacci, J.; Bhuyan, M.; Sarma, K. K.; Sharma, P.; Misra, A.; Misra, D. D.; Guha, K. Considerations on Novel Network Edge Physical Nodes Architectures Leveraging A Bottom-Up Technology-Centric Approach Empowered by Micro/Nanotechnologies (MEMS/NEMS) and Federated Learning. Preprints 2024, 2024110603. https://doi.org/10.20944/preprints202411.0603.v1

Abstract

The forthcoming paradigms of 6G, Future Networks (FN) and Super-Internet of Things (IoT), will bring disruption at various levels of the physical infrastructure. This work focuses on the network edge, stressing how the forecasted proliferation and technology innovation of the physical systems at the boundary of the network (edge), will pose crucial issues to be addressed in the next years. In particular, the continuity from the edge to the core of the network will be addressed, suggesting unprecedented design and development approaches. Along such a direction, the impact of Micro and Nanotechnologies as a Key Enabling Technology (KET) for the network edge of the future is sketched, including numerous examples of already existing micro/nano devices, components and systems. Afterwards, a practical study of how improving hardware technologies at the edge can be beneficial in terms of more efficient operation is reported. To this end, scattered intelligence shall be a key enabler with federated learning at the base station providing decisive assistance to adaptive downlink beamforming codebook design for mmWave massive MIMO set-ups as essential ingredients for enhanced link reliability in edge networks.

Keywords

6G; Future Networks (FN); Internet of Things (IoT); Distributed Computing Continuum Systems (DCCS); network edge; Micro/Nanotechnologies; MEMS; NEMS; federated learning; MIMO; beamforming 

Subject

Engineering, Electrical and Electronic Engineering

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