Learning-driven MU-MIMO Grouping for Multi-User Multimedia Applications Over Commodity WiFi
Authors: 
Hannaneh Barahouei Pasandi, 
Hadi Amirpour, Tamer Nadeem, 
Christian TimmererAuthors Info & Claims
Hannaneh Barahouei Pasandi, Hadi Amirpour, Tamer Nadeem, Christian TimmererAuthors Info & ClaimsPages 15 - 21
Abstract
MU-MIMO is a high-speed technique in IEEE 802.11ac and upcoming ax technologies that improves spectral efficiency by allowing concurrent communication between one Access Point and multiple users. In this paper, we present LATTE, a novel framework that proposes MU-MIMO-aware optimization for multi-user multimedia applications over IEEE 802.11ac/ax. Taking a cross-layer approach, LATTE first optimizes the MU-MIMO user group selection for the users with the same characteristics in the PHY/MAC layer. It then optimizes the video bitrate for each group accordingly. We present our design and its evaluation on smartphones and laptops over 802.11ac WiFi. Our experimental evaluations indicate that LATTE can outperform other video rate adaptation algorithms.
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- Learning-driven MU-MIMO Grouping for Multi-User Multimedia Applications Over Commodity WiFi
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Published In
December 2021
31 pages
ISBN:9781450391375
DOI:10.1145/3488662
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Published: 07 December 2021
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CoNEXT '21
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CoNEXT '21: The 17th International Conference on emerging Networking EXperiments and Technologies
December 7, 2021
Virtual Event, Germany
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