ECHO: Enhanced Conditional Handover boosted by Trajectory Prediction

Conditional handover (CHO) has been introduced in 5G to improve mobility robustness, namely, to reduce the number of handover failures by preparing target Base Stations (BSs) in advance and allowing the user to decide when to make a handover. This algorithm constantly prepares and releases BSs, thereby adapting to the fast changing radio condition. A user might make a handover to a distant BS that has a favorable channel only for a short time due to signal fluctuations. This increases the handover rate and might result in a Radio Link Failure (RLF) afterwards. Moreover, the constant preparation and release of BSs leads to an increased exchange of control messages between the user, the serving BS and all target BSs. Hence, there is a need to carefully select the target BSs. Therefore, we propose the Enhanced CHO (ECHO) scheme that uses trajectory prediction to prepare the BSs along the user's path. To achieve this, we also propose a Sequence to Sequence (Seq2Seq) mobility prediction model. ECHO with only one prepared BS (ECHO-1) outperforms CHO with three prepared BSs. ECHO-1 reduces the handover rate by 23 percent and the RLF rate by 77 percent, while also reducing the number of control messages in the network by 69 percent.