To satisfy the increasing demand for wireless systems capacity, the industry is dramatically increasing the density of the deployed networks. Like other wireless technologies, Wi-Fi is following this trend, particularly because of its increasing popularity. In parallel, Wi-Fi is being deployed for new use cases that are atypically far from the context of its first introduction as an Ethernet network replacement. In fact, the conventional operation of Wi-Fi networks is not likely to be ready for these super dense environments and new challenging scenarios. For that reason, the high efficiency wireless local area network (HEW) study group (SG) was formed in May 2013 within the IEEE 802.11 working group (WG). The intents are to improve the "real world" Wi-Fi performance especially in dense deployments. In this context, this work proposes a new centralized solution to jointly adapt the transmission power and the physical carrier sensing based on artificial neural networks. The major intent of the proposed solution is to resolve the fairness issues while enhancing the spatial reuse in dense Wi-Fi environments. This work is the first to use artificial neural networks to improve spatial reuse in dense WLAN environments. For the evaluation of this proposal, the new designed algorithm is implemented in OPNET modeler. Relevant scenarios are simulated to assess the efficiency of the proposal in terms of addressing starvation issues caused by hidden and exposed node problems. The extensive simulations show that our learning-based solution is able to resolve the hidden and exposed node problems and improve the performance of high-density Wi-Fi deployments in terms of achieved throughput and fairness among contending nodes.

No abstract

Recently, the High Efficiency WLAN or simply HEW study group was created within IEEE 802.11 working group. This study group considers the improvement of spectrum efficiency to enhance the system's area throughput in high density scenarios. Subsequently, this led to the creation of a new task group called 802.11ax, which is expected to deliver the next Wi-Fi generation. A key perspective considered by the recent discussions is increasing the spatial reuse using Physical Carrier Sensing (PCS) adaptation. While Transmit Power Control (TPC) has always been the chosen technique when targeting spatial reuse improvements, this work investigates the weakness points in TPC especially outside centralized network architectures. On the other hand, the incentives behind adopting the PCS approach are discussed. Accordingly, a new algorithm is proposed to adapt the PCS dynamically. The performance of this proposal is compared to that of TPC using OPNET simulations. For a dense IEEE 802.11n network topology, simulation results show that PCS outperforms TPC (120% versus 66% of throughput gain respectively). Particularly, the PCS approach is more robust when there are some nodes that are not implementing the PCS nor the TPC adaptation.