@inproceedings{679d5f2b4a4149e7a1b33d35aa6a21f7,
title = "Representation Learning for Spatial Reuse in IEEE 802.11ax-Compliance Edge Intelligence",
abstract = "IEEE 802.11ax standard supports dense deployment of access points (APs)/edge devices, with a focus on robustness and uplink transmission. Dense deployments of IEEE 802.11ax APs use virtual carrier sensing to mitigate the effects of interference. Other challenges of IEEE 802.11ax compatible edge devices under dense deployment include homogeneous and heterogeneous coexistence and backward compatibility with legacy devices. To address these challenges, in this paper, two representation learning approaches based on graph neural network (GNN), called as direct-affinityGNN and skip-affinityGNN. Extensive evaluations demonstrate the effectiveness of both the approaches to enable high-capacity edge intelligence.",
keywords = "Edge intelligence, IEEE 802.11ax WLANs, Representation learning for networks",
author = "Stephen Azeez and Shagufta Henna",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 8th International Congress on Information and Communication Technology, ICICT 2023 ; Conference date: 20-02-2023 Through 23-02-2023",
year = "2024",
doi = "10.1007/978-981-99-3236-8_36",
language = "English",
isbn = "9789819932351",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "459--470",
editor = "Xin-She Yang and Sherratt, {R. Simon} and Nilanjan Dey and Amit Joshi",
booktitle = "Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023",
}