… In this paper, we first design a self-learning architecture applicable to the SDN-based WLAN
… handoff management scheme based on deep reinforcement learning, specifically deep Q-…

… Handover (HO). HO events become frequent for an ultra-dense dense network scenario, and
HO management … HO control based on the offline reinforcement learning (RL) algorithm that …

… Taking into account the dynamic of the environment, we propose a deep reinforcement
learning (DRL) based approach to solve the formulated nonconvex problem of minimizing …

… In this work, we have proposed a framework to manage handover events based on multi-agent
deep reinforcement learning. We maximize the average network sum-rate taking into …

… In this paper, we propose an offline scheme based on double deep reinforcement learning
(DDRL) to minimize the frequency of HOs in mm-wave networks, which subsequently …

… We establish an HO management and power … reinforcement learning (MARL) algorithm
based on the proximal policy optimization (PPO) method, by introducing the centralized training …

… In this work, the user handoff problem in slice-based mobile … to reinforcement learning
theory [6, 7] and propose an intelligent handoff algorithm based on deep reinforcement learning. …

… In our future research, additional parameters such as time to trigger and reinforcement
learning algorithm can be taken into consideration to ensure the quality of experience. …

… and communication cost, and resource allocation for the vehicle is done, using Deep
Reinforcement Learning (DRL), during handoff. The contributions of this study are as follows: …

… While making handover decisions, it is important to predict … proactive framework wherein
handover timings are optimized … use a deep reinforcement learning for deciding the handover …