… network selection decision was based on TOPSIS algorithm. The parameters that are
evaluated in this work are handover success probability, handover failure, unnecessary handover, …

… Near-RT RIC where the machine learning algorithm to control the handover process can
be … -learning-based handover algorithm in Near-RT RIC to control the handover process …

… However, when a handover failure (HOF) occurs, the interruption time increases … “ZEro
handover failure with Unforced and automatic time-to-execute Scaling” (ZEUS) algorithm to …

… Handover decision using deep stacked autoencoder This section presents a Deep stacked
autoencoder for a handover … as the input to a deep-stacked autoencoder. The advantages of …

… likely to make a handover. Artificial neural networks have recently proven to be useful in
learning complex patterns from data [3], thus, we enhance CHO with a Deep Learning (DL) …

… , we learn to predict the deterioration patterns of potential target links post HO. We use the
Jump Markov Linear System (JMLS) and Deep Reinforcement Learning (DRL) to learn the …

… Second, we adopt the Q-learning framework to learn the optimal HO policy from the
environment as fuzzy rules for the fuzzy inference system. This approach allows the FLHA to self-…

… In this paper, in order to solve the shortcomings of the algorithms mentioned above and …
we propose a handover method exploiting the DQN framework of the adaptive learning rate with …

… Then, we show an impact of the handover cost on capacity achieved by all algorithms. …
HPR of all algorithms for the handovers performed by the FlyBSs (subplot a) and the handovers …

… Deep learning is also used to perform online user association in massive MIMO 4G
networks, where inputs to the neural network are only UEs locations [17]. For mobile mmWave …