Next-generation wireless networks are expected to support extremely … wireless radio technology
paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and …

… If we can conduct the optimization operations automatically, network optimization will be
easier to … Within ALF, we propose several potential paradigms, including automatic model …

… DL for addressing wireless network optimization problems, … of DL comes regarding the
wireless network optimization. In … by the conventional optimization paradigm, DL technologies …

… of neural networks, thereby reducing the required training data … wireless network optimization,
this article first proposes a holistic framework of knowledge-driven DL in wireless networks …

… and width of deep neural networks), and data volume together lead to optimization tasks with
… This also necessitates the use of scalable models to decompose a large optimization prob…

… of developing a new design paradigm for wireless transmission systems by … network
structure attains a good performance. Since the training process itself is actually an optimization …

… learning. Furthermore, we investigate their employment in the compelling applications of
wireless networks, including heterogeneous networks (… ML paradigms in wireless networks. …

… Q-learning may be invoked for finding an optimal action policy for any given finite Markov
decision process, especially when the system model is unknown. It is a model-free …

… learning strategies from the optimization perspective where the … learning for IoT systems
do not cover learning paradigms used for design objectives in IoT systems and identify learning …

… In recent years, a range of surveys have been conceived on machine learning paradigms.
Some of them focused their scope on a specific wireless scenario, such as WSNs [24], [25], …