… machine learning models to predict the strength of the radio signals. BACKGROUND: The
propagation model is often used to determine the optimal location of radio … an environment for …

… This article provides an overview of online machine learning methods for optimizing wireless
networks in the presence of multiple users and multiple reconfigurable intelligent surfaces. …

… of the environment and makes decisions on how the radio should … Generic learning-based
cognitive radio is a relatively … as genetic algorithms to evolve radio parameters with the goal of …

… We describe in detail several challenging learning issues that arise in cognitive radio
networks (CRNs), in particular in non-Markovian environments and decentralized networks, and …

… are promising for identifying the properties of the indoor radio environment (RE) without …
with machine learning. To train the models and assess their performance, we acquired radio …

… that perceive and adapt to the wireless environments in Sections II and III, respectively. In
Section IV, we present powerful machine learning algorithms that enhance the perception …

… (CR) is used to solve the spectrum underutilization problem and to sense the radio
environment to detect spectrum holes in terms of both time and domain [1]. Cognitive …

… This letter presents a machine learning approach for indoor environment classification … of
the radio frequency (RF) signal in a realistic environment. Several machine learning classifica…

… In this work, we propose a novel crowdsourced REM method which exploits machine
learning techniques to choose crowdsourced data for radio field intensity interpolation. The …

… Furthermore, where there is no prior knowledge of the environment, machine learning can
still make predictions based on the features available in the environment and come up with …