Yan Long

TL;DR: This survey conducts a comprehensive review on the medium access control layer (MAC) related issues for the IEEE 802.11ay, including spatial sharing and interference mitigation technologies, and presents an in-depth survey on beamforming training, beam tracking, single- user multiple-input-multiple-output beamforming, and multi-user multiple- input- multiple- Output beamforming.

TL;DR: Deep learning could be a powerful tool to mitigate the complexity of RRM problems in dense mmWave networks through a deep learning-based beam management and interference coordination method, through which the conventional complex BM-IC algorithm is transformed into a deep neural network (DNN)-based approximation.

TL;DR: This paper proposes a fast beam tracking mechanism to cope with the problem of beam misalignment between UAVs or between the UAV group leader and relay base station/satellite caused by the movement of UAV(s), and a self-healing mechanism to find alternative links to restore the network capability.

TL;DR: In this paper, the authors provide a comprehensive survey on the key medium access control (MAC) layer techniques and physical layer (PHY) techniques being discussed in the Extremely High Throughput (EHT) task group, including the channelization and tone plan, multiple resource units (multiRU) support, 4096 quadrature amplitude modulation (4096-QAM), preamble designs, multiple link operations (e.g., multi-link aggregation and channel access), multiple input multiple output (MIMO) enhancement, multiple access point (multi-AP)

TL;DR: A comprehensive survey on the key medium access control (MAC) layer techniques and physical layer (PHY) techniques being discussed in the EHT task group, including the channelization and tone plan, multiple resource units (multi-RU) support, and the related latest progresses from worldwide research are provided.