… learning over wireless networks, as discussed next. … With time-varying networks, learning
agents are affected by loss of … essential for learning over dynamic wireless network topologies. …

… , we apply the concept of reinforcement learning and implement a deep Q-network (DQN).
We … Finally, we propose an adaptive DQN approach with the capability to adapt its learning in …

… learn from the continuous learning mechanism in the biological immune system to realize
the learning … Motivated by this, this paper proposes a continual learning classification method …

… Extensive motivation is given for why deep learning based on artificial neural networks will
… of future wireless communication networks, and our vision of how artificial neural networks …

… Furthermore, Chen et al. proposed a multitask continual learning methods … network, but it
only achieves continual learning in some similar tasks [15]. In summary, most continual learning …

… a Continual Learning framework to develop intelligent location-based services. In particular,
we show how Digital Twins supports the development of complex Continual Learning …

… how the performance of traditional machine learning (ML)-based … This paper explores
continual learning (CL) techniques for … We train a vanilla neural network (VNN) model in the CL …

… However, we argue that historical-data based continual learning method for traffic prediction
may … His research interests include wireless networks, distributed systems, data mining, and …

… Reinforcement learning (RL) is known as a model-free and highly efficient intelligent …
management problems in wireless networks. However, for large-scale networks with high latency …

… crafted not only to accelerate the learning process, but also to … in the context of multi-access
wireless systems. More specifically, … problem for NUFM over wireless networks, capturing the …