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D3.6 Report on AI-based MIMO and mmWave Communications

Authors: Tarrías, Antonio; Wang, Xiyu; Adeogun, Ramoni; id_orcid 0000-0003-1118-7141; Nordio, Alessandro; Virone, Giuseppe; Serdar Tan, Ahmet; Ognenoski, Ognen; +13 Authors

D3.6 Report on AI-based MIMO and mmWave Communications

Abstract

This report documents findings and conclusions of AI-enhanced MIMO methods and mmWave communications for 6G Air Interface. It includes the contributions of CENTRIC WP3 on AI-based beam management and sensing in mmWave communications and AI-empowered MIMO communications. The contributions for AI-based beam management and sensing target challenges in beam tracking and prediction, and environment specific beam management. The contributions for AI-empowered MIMO target challenges in enhanced CSI acquisition, MIMO pre-processing and learning based MIMO receivers. Overall, the report demonstrates the feasibility and benefits of AI/ML based methods for 6G Air Interface.

Country
Denmark
Keywords

MIMO, ISAC, mmWave, Neural receiver, PHY Security, CSI, AI, Sensing, Beam management

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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