… allocation given channel assignment. Then, with the optimal power allocation, we propose
to utilize the deep reinforcement learning to learn an optimal channel assignment policy. To …

… The performance of hybrid NOMA systems depends on resource allocation including power
and channel. In this paper, we focus on the channel assignment. Since channel assignment …

… 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 …

… the channel assignment problem … reinforcement learning (RL) (eg, Barto, Bradtke and Singh,
1995, or the recent textbook by Bertsekas and Tsitsiklis, 1996). Our method learns channel …

… Umehira, "Dynamic channel assignment scheme with … Wang, "Dynamic channel assignment
based on interference … centralized dynamic channel assignment with channel segregation …

… channel assignment (DCA) problem by using a form of realtime reinforcement learning
known as Q-learning in … is designed to learn an optimal channel assignment policy by directly …

… channel assignment (DCA) problem through a form of real-time reinforcement learning known
as Q learning. … , the system is designed to learn an optimal assignment policy by directly …

… learn the proper policies of its channel selection and backoff adaptation with the goal of avoiding
channel … achieve collaborative optimization of their channel assignments, we propose a …

This paper investigates a new medium access control (MAC) protocol for multi-channel
heterogeneous networks (HetNets) based on deep reinforcement learning (DRL), referred to as …

… the problem of joint channel assignment and power … channel information might not be viable
in C-V2X systems with large number of users. Utilizing a multi-agent reinforcement learning (…