Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with high …
Future wireless communication networks tend to be intelligentized to accomplish the missions that cannot be preprogrammed. In the new intelligent communication systems …
Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Such model-based methods tend to be sensitive to …
The fields of machine learning and mathematical programming are increasingly intertwined. Optimization problems lie at the heart of most machine learning approaches. The Special …
L Deng, D Yu - Microsoft research monograph, 2013 - microsoft.com
This short monograph contains the material expanded from two tutorials that the authors gave, one at APSIPA in October 2011 and the other at ICASSP in March 2012. Substantial …
Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model …
Mobile Edge Artificial Intelligence: Opportunities and Challenges presents recent advances in wireless technologies and nonconvex optimization techniques for designing efficient edge …
M Kiran, M Ozyildirim - arXiv preprint arXiv:2201.11182, 2022 - arxiv.org
Reinforcement learning (RL) applications, where an agent can simply learn optimal behaviors by interacting with the environment, are quickly gaining tremendous success in a …
The ever-increasing demand for wireless services and the continual improvements in wireless technology has led to the emergence of different types of wireless networks. These …