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Recent advancements in wireless local area network (WLAN) technology include IEEE 802.11be and 802.11ay, often known as Wi-Fi 7 and WiGig, respectively. The goal of these developments is to provide Extremely High Throughput (EHT) and low latency to meet the demands of future applications like as 8K videos, augmented and virtual reality, the Internet of Things, telesurgery, and other developing technologies. IEEE 802.11be includes new features such as 320 MHz bandwidth, multi-link operation, Multi-user Multi-Input Multi-Output, orthogonal frequency-division multiple access, and Multiple-Access Point (multi-AP) coordination (MAP-Co) to achieve EHT. With the increase in the number of overlapping APs and inter-AP interference, researchers have focused on studying MAP-Co approaches for coordinated transmission in IEEE 802.11be, making MAP-Co a key feature of future WLANs. Moreover, similar issues may arise in EHF bands WLAN, particularly for standards beyond IEEE 802.11ay. This has prompted researchers to investigate the implementation of MAP-Co over future 802.11ay WLANs. Thus, in this article, we provide a comprehensive review of the state-of-the-art MAP-Co features and their shortcomings concerning emerging WLAN. Finally, we discuss several novel future directions and open challenges for MAP-Co., Comment: The reason for the replacement of the previous version of the paper is due to a change in the author's list. As a result, a new version has been created, which serves as the final draft version before acceptance. This updated version contains all the latest changes and improvements made to the paper

WiGig networks and 60 GHz frequency communications have a lot of potential for commercial and personal use. They can offer extremely high transmission rates but at the cost of low range and penetration. Due to these issues, WiGig systems are unstable and need to rely on frequent handovers to maintain high-quality connections. However, this solution is problematic as it forces users into bad connections and downtime before they are switched to a better access point. In this work, we use Machine Learning to identify patterns in user behaviors and predict user actions. This prediction is used to do proactive handovers, switching users to access points with better future transmission rates and a more stable environment based on the future state of the user. Results show that not only the proposal is effective at predicting channel data, but the use of such predictions improves system performance and avoids unnecessary handovers., Comment: Submitted to IEEE Network