Wi-Fi

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 …

IEEE 802.11bn multi-AP coordinated spatial reuse with hierarchical multi-armed bandits

Coordination among multiple access points (APs) is integral to IEEE 802.11bn (Wi-Fi 8) for managing contention in dense networks. This letter explores the benefits of Coordinated Spatial Reuse (C-SR) and proposes the use of reinforcement learning to …

Using ranging for collision-immune IEEE 802.11 rate selection with statistical learning

Appropriate data rate selection at the physical layer is crucial for Wi-Fi network performance: too high rates lead to loss of data frames, while too low rates cause increased latency and inefficient channel use. Most existing methods adopt a probing …

Reinforced-lib: Rapid prototyping of reinforcement learning solutions

Reinforcement learning (RL) is emerging as a promising framework for training intelligent agents to solve complex problems. However, developing RL solutions involves a complex process that requires experimenting with different models, agents, and …

FTMRate: Collision-Immune Distance-based Data Rate Selection for IEEE 802.11 Networks

Data rate selection algorithms for Wi-Fi devices are an important area of research because they directly impact performance. Most of the proposals are based on measuring the transmission success probability for a given data rate. In dense scenarios, …

Improving IEEE 802.11ax UORA Performance: Comparison of Reinforcement Learning and Heuristic Approaches

Machine learning (ML) has gained attention from the network research community because it can help solve difficult problems and potentially lead to groundbreaking achievements. In the Wi-Fi domain, ML is applied to solve challenges such as efficient …

DB-LBT: Deterministic Backoff with Listen Before Talk for Wi-Fi/NR-U Coexistence in Shared Bands

The legacy approach to solve coexistence problems between multiple wireless networks operating in the same frequency bands is through network planning. However, this approach is often unfeasible in unlicensed (shared) bands, where different network …

Using self-deferral to achieve fairness between Wi-Fi and NR-U in downlink and uplink scenarios

Wireless networks operating in unlicensed bands generally use one of two channel access paradigms: random access (e.g., Wi-Fi) or scheduled access (e.g., LTE License Assisted Access, LTE LAA and New Radio-Unlicensed, NR-U). The coexistence between …