Wi-Fi

Learning-based channel access in Wi-Fi: a multi-armed bandit approach

Due to its static protocol design, IEEE 802.11 (aka Wi-Fi) channel access lacks adaptability to address dynamic network conditions, resulting in inefficient spectrum utilization, unnecessary contention, and packet collisions. This paper investigates …

Performance evaluation of multi-armed bandit algorithms for Wi-Fi channel access

The adoption of dynamic, self-learning solutions for real-time wireless network optimization has recently gained significant attention due to the limited adaptability of existing protocols. This paper investigates multi-armed bandit (MAB) strategies …

Exploring low-latency and high-reliability Wi-Fi with Co-RTWT

Emerging real-time applications in industrial and consumer wireless networks impose stringent requirements on latency and reliability. To address these demands, IEEE 802.11bn introduces the Coordinated Restricted Target Wake Time (Co-RTWT) mechanism, …

Deep reinforcement learning-based scheduling for Wi-Fi multi-access point coordination

Multi-access point coordination (MAPC) is a key feature of IEEE 802.11bn, with a potential impact on future Wi-Fi networks. MAPC enables joint scheduling decisions across multiple access points (APs) to improve throughput, latency, and reliability in …

A deterministic backoff approach for Wi-Fi and NR-U coexistence in shared bands

In unlicensed (shared) bands, wireless technologies typically operate without central coordination, which can lead to unwanted transmission interruptions, collisions, and resource wastage. We focus on Wi-Fi and NR-U coexistence in shared bands and …

Indoor positioning with Wi-Fi Location: a survey of IEEE 802.11mc/az/bk fine timing measurement research

Indoor positioning is an enabling technology for home, office, and industrial network users because it provides numerous information and communication technology (ICT) and Internet of things (IoT) functionalities such as indoor navigation, smart …

Coordinated spatial reuse scheduling with machine learning in IEEE 802.11 MAPC networks

The densification of Wi-Fi deployments means that fully distributed random channel access is no longer sufficient for high and predictable performance. Therefore, the upcoming IEEE 802.11bn amendment introduces multi-access point coordination (MAPC) …

Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi

Multi-Access Point Coordination (MAPC) and Artificial Intelligence and Machine Learning (AI/ML) are expected to be key features in future Wi-Fi, such as the forthcoming IEEE 802.11bn (Wi-Fi 8) and beyond. In this paper, we explore a coordinated …

Toward specialized wireless networks using an ML-driven radio interface

Future wireless networks will need to support diverse applications (such as extended reality), scenarios (such as fully automated industries), and technological advances (such as terahertz communications). Current wireless networks are designed to …

Machine Learning and Wi-Fi: Unveiling the Path Toward AI/ML-Native IEEE 802.11 Networks

Artificial intelligence (AI) and machine learning (ML) are nowadays mature technologies considered essential for driving the evolution of future communications systems. Simultaneously, Wi-Fi technology has constantly evolved over the past three …