We study the problem of selecting the contention window (CW) for age of information (AoI)-
oriented IEEE 802.11 networks using deep reinforcement learning (DRL) techniques. AoI …

The ubiquitous deployment of IEEE 802.11 based Wireless Local Area Networks (WLANs)
or WiFi networks has resulted in dense deployments of Access Points (APs) in an effort to …

The rapid evolution of wireless communication technologies has led to the emergence of 6G
networks, which promise unprecedented levels of connectivity, capacity, and intelligence …

Pomocą w rozwiązaniu wyzwań stojących przed kolejnymi generacjami lokalnych sieci
bezprzewodowych mogą okazać się metody oparte na uczeniu maszynowym (machine …

On-device Machine Learning (on-deviceML) is the concept of bringing Machine Learning
models to the constraint device itself and making it smarter. Tiny Machine Learning (TinyML) …

While dynamic channel bonding (DCB) is proven to boost the capacity of wireless local area
networks (WLANs) by adapting the bandwidth on a per-frame basis, its performance is tied …

The number of networked devices is increasing rapidly with numbers reaching 29.3 billion
by 2023, up from 18.4 billion in 2018. This increasing user density requires new …

Learning-based Contention Window Avoidance Scheme aimed at improving service priority
differentiation and network throughput under high load conditions within wireless networks …

In this thesis, we focus on the so-called spectrum management's joint problem: efficient
allocation of primary and secondary channels in channel bonding wireless local area …