… Handover is an essential and significant component of mobility management in cellular
networks. Handover management is more challenging for Fifth Generation (5G) networks …

… trigger decision, we implement the optimization theory to manage the resource allocation,
target BS and beam selection in each SCBS, which not only improves the overall system …

… ability of reinforcement learning, this paper proposes an adaptive optimization method based
on the Q-Learning algorithm to achieve real-time estimation of the handover parameters of …

… management has been explored with machine and artificial intelligence (AI) learning … ) [8,9]
and deep reinforcement learning (DRL) to learn the feasible optimal deterioration pattern that …

… handover strategy based on multi-agent reinforcement learning that aims to minimize average
satellite handovers while … Pavlidou, “Dynamic time-based handover management in LEO …

… accelerate the learning. We adopt the reinforcement learning (RL) framework to learn the
optimal … Sorour, TY Al-Naffouri, and MS Alouini, “Handover management in 5g and beyond: A …

… reinforcement learning (RL) has the ability to learn to make optimal decisions by interacting
with an environment via trial and error. Additionally, deep learning … , “Handover management …

… manage the handover of users to improve reliability in a high-mobility scenario using deep
learning. … a BS, which is done without adding a handover coefficient. Then, we train the neural …

… In this work, a deep reinforcement learning method was proposed to provide a centralized
control of multiple users in order to enhance the data rate and avoid unnecessary …

… The current handover (HO) triggering mechanism A3 event was designed only for mobility
management in the macrosystem. Directly implementing A3 in 5G-HetNets may degrade the …