IEEE 802.11ax: challenges and requirements for future high efficiency wifi

The popularity of IEEE 802.11 based wireless local area networks (WLANs) has increased significantly in recent years because of their ability to provide increased mobility, flexibility, and ease of use, with reduced cost of installation and maintenance. This has resulted in massive WLAN deployment in geographically limited environments that encompass multiple overlapping basic service sets (OBSSs). In this article, we introduce IEEE 802.11ax, a new standard being developed by the IEEE 802.11 Working Group, which will enable efficient usage of spectrum along with an enhanced user experience. We expose advanced technological enhancements proposed to improve the efficiency within high density WLAN networks and explore the key challenges to the upcoming amendment.Peer ReviewedPostprint (author's final draft

WLAN Channel Selection Without Communication

In this paper we consider how a group of wireless access-points can self-configure their channel choice so as to avoid interference between one another and thereby maximise network capacity. We make the observation that communication between access points is not necessary, although it is a feature of almost all published channel allocation algorithms. We argue that this observation is of key practical importance as, except in special circumstances, interfering WLANs need not all lie in the same administrative domain and/or may be beyond wireless communication distance (although within interference distance). We demonstrate the feasibility of the communicationfree paradigm via a new class of decentralized algorithms that are simple, robust and provably correct for arbitrary interference graphs. The algorithm requires only standard hardware and we demonstrate its effectiveness via experimental measurements

Analytical Performance Evaluation of Cooperative and Multi-Radio Concepts in Emerging Wireless Networks

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Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks

Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this paper, we devise the role of network simulators for bridging the gap between ML and communications systems. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights on the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network