… In this paper, we contrast solutions that are based on (and even improve) state of the art
heuristics to a data-driven solution that leverages Deep Reinforcement Learning (DRL). Based …

… We consider in this paper not only channel assignment task but also the call admission
control problem in a cellular network. We consider a DCA system handling not only one class of …

… identification and appropriate channel assignment to reduce overall latency. In [12], the
authors’ objectives are to maximize the system throughput in multi-channel cognitive vehicular …

… Researches [16], [17] find the optimal channel assignment with fixed power allocation, and
then select the optimal power allocation to maximize network utility with the fixed channel. …

… Although Q-learning has been considered for power allocations and channel assignments,
it has not been used, to the best of the authors’ knowledge, for adapting a dynamic EDT for …

… channel scheduling can allocate or recycle free channels according to service requirements.
The Q-Learning algorithm in reinforcement learning meets channel … Channel assignment is …

… In Section 2, we present related works including MACs protocol and deep learning for
channel assignment, and deep reinforcement learning for channel assignment and traffic control. …

… approach, which is an advanced reinforcement learning … TensorFlow to implement deep
reinforcement learning in this paper … Haykin, “A Q-learning-based dynamic channel assignment …

… reinforcement learning based channel bonding adaptation algorithm termed PoBA is developed
to solve the hidden channel … channel bonding in WLANs focus on spectrum assignment. …

… Our reinforcement learning algorithm used the overlapping windows with window size (SW)
set to 2. This value was set to increase the influence of consecutive interference for a …