Novel learning-based spatial reuse optimization in dense WLAN deployments

Abstract

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.

Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs

“…For those reasons, in this paper, we focus on the suitability of supervised learning methods, mostly based on deep learning (DL), for the SR problem in WLANs. To the best of our knowledge, this approach has not been studied before in the context of SR. A centralized DL-based method was proposed in [25] to jointly select the transmission power and the CCA, but not in the context of 11ax SR operation. DL was also applied in [26] to address the channel bonding problem in dense WLANs.…”

Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information

“…With the recent advance of artificial intelligence, machine learning techniques were applied to the problem of spatial reuse. Jamil et al [29] proposed a centralized approach where a central coordinator collects information and runs a neural network to find the best transmit power and CST to use. Wilhelmi et al [30,31] proposed a distributed method where each node runs multi-armed bandits to find the best channel and transmit power.…”

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

“…As shown in the referred papers, in multi-agent scenarios where the agents compete with each other without collaborating, convergence may be hard or impossible to achieve. There are also papers using SL techniques, such as NNs [129], [155], to help with the selection of proper SR parameters (transmission power and sensitivity levels) given the characteristics of the scenario are known.…”

“…The models are trained offline using a dataset that covers multiple scenarios and configurations. A different approach is considered in [155], where a central controller able to configure the entire Wi-Fi network is considered. A NN is then used to propose configurations to all BSSs so spatial reuse is maximized.…”