Responsive spectrum management for wireless local area networks: from heuristic-based policies to model-free reinforcement learning
Published in Unknown Venue, 2021
Recommended citation: Sergio Barrachina Muñoz (2021). Responsive spectrum management for wireless local area networks: from heuristic-based policies to model-free reinforcement learning. Unknown Venue. https://dialnet.unirioja.es/servlet/tesis?codigo=292244
Abstract: 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 networks (WLANs). From IEEE 802.11 n to more recent standards like 802.11 ax and 802.11 be, bonding channels together is permitted to increase transmissions’ bandwidth. While such an increase favors the potential network capacity and the activation of higher transmission rates, it comes at the price of reduced power per Hertz and accentuated issues on contention and interference with neighboring nodes. So, if WLANs were per se complex deployments, they are becoming even more complicated due to the increasing node density and the new technical features required by novel highly bandwidth-demanding applications. This dissertation provides an in-depth study of channel allocation and channel bonding in WLANs and discusses the suitability of solutions ranging from heuristic-based to reinforcement learning (RL)-based.