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 …
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 …
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, …
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 …
Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable …
The proper setting of contention window (CW) values has a significant impact on the efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is not scalable enough to maintain stable throughput for an increasing number …