Improving spatial reuse in future dense high efficiency Wireless Local Area Networks. (Amélioration de la réutilisation spatiale pour les futurs réseaux locaux sans fil à haute densité)

Despite their remarkable success, the first widely spread versions of the Institute of Electrical and Electronics Engineers (IEEE) 802.11 Wireless Local Area Network (WLAN) standard, IEEE 802. 11 a/b/g, featured low spectral efficiencies that are becoming insufficient to satisfy the explosive growth in capacity and coverage demands. Thanks to the advances in the communication theory and the use of the 5 GHz frequency band, the IEEE 802.11n and recently the IEEE 802.1lac amendments improved the Physical Layer (PHY) data rates by introducing Multiple-Input Multiple Output (MIMO) techniques, higher Modulation and Coding Scheme (MCS), etc. Today, after almost two decades of its first appearance, Wi-Fi is presented as a gigabit wireless technology. However, the full potential of the latest PHY layer advances cannot be enabled in all real world deployment scenarios. With the rapidly increasing density of WLAN deployments and the huge popularity of Wi-Fi enabled devices, spatial reuse must be optimized. On another hand, the new challenging use case environments and the integration of mobile networks mainly for cellular offloading are limiting the opportunity of the current Wi-Fi generations to provide better quality at lower cost.In this thesis, we contribute to the current standardization efforts aiming to leverage the Wi-Fi efficiency in high density environments. At the time of writing this document, the IEEE 802.11ax Task Group (TG) is developing the specification for the High Efficiency WLAN (HEW) standard (next Wi-Fi evolution). Rather than continuing to target increased theoretical peak throughputs, we focus in the context of HEW on improving the throughput experienced by users in real life conditions where many other devices, belonging to neighboring overlapping networks, simultaneously contend to gain access. To enhance this performance, we propose a dynamic adaptation of the carrier sensing mechanism. Compare to controlling the transmission power, the proposed mechanism has more incentives because it benefits directly the concerned user. Extensive simulation results show impor1ant throughput gains in dense scenarios. Then, we study the impact of the new adaptation on the current rate control algorithms. We find that our adaptation mechanism operates efficiently without substantially modifying these algorithms that are widely used in today's operating WLANs. Furthermore, after analyzing the fairness performance of the proposed adaptation, we devise a new approach to jointly adapt the carrier sensing and the transmission power in order to preserve higher fairness degrees while improving the spatial reuse. This approach is evaluated in different dense deployment scenarios where it proves its capability to resolve the unfairness issues especially in the presence of legacy nodes in the network, while improving the achieved throughput by 4 times compared to the standard performance. Finally, we design and implement centralized learning-based solution that uses also an approach based on joint adaptation of transmission power and carrier sensing. This new solution takes benefit from the capability of artificial neural networks to model complex nonlinear functions to optimize the spatial reuse in dense WLANs while preserving fairness among contending nodes. The different contributions of this work have helped bring efficient solutions for future WiFi networks. We have presented these solutions to the IEEE 802.11ax TG where they were identified as important potential technical improvements for the next WLAN standard.