We propose an access control scheme of devices using Q-learning algorithm. Experimental results show that the scheme adaptively adjusts access control ...
Bello et al. [9] employed a Q-learning mechanism to intelligently assign the access time-slots to the MTC devices while Moon and Lim [10] applied a Q-learning ...
This work proposes an access control scheme of devices using Q-learning algorithm to solve the overload problem in MTC applications and shows that the ...
May 14, 2017 · In this paper, we model the problem of determining the strategic parameters with a reinforcement learning algorithm.
Access control of MTC devices using reinforcement learning approach · User assignment · Assignment remove confirmation · Reporting an error / abuse · Sending the ...
Feb 22, 2022 · In this paper, we propose a deep reinforcement learning (RL) based approach for NOMA-based random access network with truncated channel inversion power control.
In this work, we propose a delay-aware double deep reinforcement learning mechanism that can dynamically adapt two parameters of the system in order to enhance ...
We propose, in this paper, to apply the access class barring (ACB) mechanism to regulate the number of devices competing for the access.
To this end, we propose a reinforcement learning-based evolved Node-B (eNB) selection algorithm which allows the MTC devices to choose the eNBs (or base ...
Aug 1, 2021 · This article presents a simple yet effective two-step Reinforcement Learning (RL)-enabled intelligent D2D communication model, which considers relay selection ...
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