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Back [PhD thesis] Responsive spectrum management for wireless local area networks: from heuristic-based policies to model-free reinforcement learning

[PhD thesis] Responsive spectrum management for wireless local area networks: from heuristic-based policies to model-free reinforcement learning

[PhD thesis] Responsive spectrum management for wireless local area networks: from heuristic-based policies to model-free reinforcement learning

Author: Sergio Barrachina Muñoz

Supervisors: Boris Bellalta

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 802.11n to more recent standards like 802.11ax and 802.11be, 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. To characterize channel bonding in saturated WLANs, we first propose an analytical model based on continuous-time Markov networks (CTMNs). This model relies on a novel, purpose-designed algorithm that generates CTMNs from spatially distributed scenarios, where nodes are not required to be within the carrier sense range of each other. We identify the key factors affecting the throughput and fairness of different channel bonding policies and expose critical interrelations among nodes in the spatial domain. By extending the analytical model to support unsaturated regimes, we highlight the benefits of allocating channels as wide as possible all together with adaptive policies to cope with unfair situations. Apart from the analytical model, this thesis relies on simulations to generalize channel bonding in dense scenarios while avoiding costly, sometimes unfeasible, experimental testbeds. Unfortunately, existing wireless network simulators tend to be too simplistic or too computational demanding. That is why we develop the Komondor wireless network simulator, with the essential advantage over other well-known simulators lying in its high event processing rate. We then deviate from analytical models and simulations and tackle real measurements through the Wi-Fi All-Channel Analyzer (WACA), the first system specifically designed to simultaneously measure the energy in all the 24 bondable Wi-Fi channels at the 5 GHz band. With WACA, we perform a first-of-its-kind spectrum measurement in areas including urban hotspots, residential neighborhoods, universities, and even a football match in Futbol Club Barcelona’s Camp Nou stadium. Our experimental findings reveal the underpinning factors controlling throughput gain, from which we highlight the inter-channel correlation.

We show the significance of the gathered dataset for finding new insights, which would not be possible otherwise, given that simple channel occupancy models severely underestimate the potential gains. As for solution proposals, we first cover heuristic-based approaches to find satisfactory configurations quickly. In this regard, we propose dynamic-wise (DyWi), a lightweight, decentralized, online primary channel selection algorithm for dynamic channel bonding. DyWi improves the expected WLAN throughput by considering not only the occupancy of the target primary channel but also the activity in the secondary channels. Even when assuming significant delays due to primary channel switching, simulations reveal important throughput and delay improvements. Finally, we identify machine learning (ML) approaches applicable to the spectrum management problem in WLANs and justify why model-free RL suits it the most. In particular, we put the focus on the adequate performance of stateless variations of RL and anticipate multi-armed bandits as the right solution since i) we need fast adaptability to suit user experience in dynamic Wi-Fi scenarios and ii) the number of multichannel configurations a network can adopt is limited; thus, agents can fully explore the action space in a reasonable time.

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