TY - GEN
T1 - A Cascading Bandit Approach to Efficient Mobility Management in Ultra-Dense Networks
AU - Wang, Chao
AU - Zhou, Ruida
AU - Yang, Jing
AU - Shen, Cong
N1 - Funding Information:
This work was supported in part by the US National Science Foundation (NSF) under Grant ECCS-1650299.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Efficient mobility management is a key problem in modern wireless networks with high node density. In this paper, we propose an online learning approach for mobility management in ultra-dense networks, based on the cascading multi-armed bandits model. The proposed Cost-aware Cascading Bandit Neighbor Cell List (CCB-NCL) mobility protocol relies on the active neighbor cell list to assist the user equipment to explore the base station selection sequentially. Simulation results show that the proposed algorithm reduces the handover latency with lower dropped call rate, hence it is a better fit to efficient mobility management.
AB - Efficient mobility management is a key problem in modern wireless networks with high node density. In this paper, we propose an online learning approach for mobility management in ultra-dense networks, based on the cascading multi-armed bandits model. The proposed Cost-aware Cascading Bandit Neighbor Cell List (CCB-NCL) mobility protocol relies on the active neighbor cell list to assist the user equipment to explore the base station selection sequentially. Simulation results show that the proposed algorithm reduces the handover latency with lower dropped call rate, hence it is a better fit to efficient mobility management.
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U2 - 10.1109/MLSP.2019.8918838
DO - 10.1109/MLSP.2019.8918838
M3 - Conference contribution
AN - SCOPUS:85077700791
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
BT - 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019
PB - IEEE Computer Society
T2 - 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019
Y2 - 13 October 2019 through 16 October 2019
ER -