Intelligent Dual Active Protocol Stack Handover Based on Double DQN Deep Reinforcement Learning for 5G mmWave Networks

The recently proposed dual active protocol stack (DAPS) handover (HO) is one of the mobility enhancements that can effectively reduce the handover interruption time (HIT) in 5G networks. By using a DAPS solution, users can be connected to both the source cell and target cell during the HO execution phase, and thereby 0 ms of HIT becomes possible. However, the DAPS HO procedure may fail in 5G networks due to the channel characteristics of millimeter-wave (mmWave) signals. Since mmWave links are vulnerable to blockages, the received signal quality may degrade suddenly, which gives rise to an abrupt outage before DAPS HO can be completed. In this paper, a novel learning-based DAPS HO technology named intelligent DAPS (I-DAPS) HO is proposed to avoid sudden radio link failure (RLF) while providing a high data rate. The proposed I-DAPS HO uses a double deep Q-network (DDQN) deep reinforcement learning (DRL) framework, where blockage predictions are made based on past received signal data such that RLFs can be actively avoided. The performance evaluation demonstrates that the proposed I-DAPS HO scheme can effectively avoid RLF and improve the throughput performance compared to advanced 5G HO schemes.