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Higher frequencies that are introduced in 5G networks cause rapid signaldegradation and challenge user mobility. In recent studies, a conditionalhandover procedure has been adopted for 5G networks to enhance user mobilityrobustness. In this paper, mobility performance of the conditional handover isanalysed for 5G mm-Wave systems with beamforming. In addition, a resourceefficient random access procedure is proposed that increases the chance ofcontention-free random access during handover, which reduces signaling andinterruption time. Moreover, simple, yet, effective decision tree basedsupervised learning method is proposed to minimize the handover failures thatare caused by beam preparation phase of random access procedure. Results revealthe trade-off between contention free random access and handover failures. Itis also shown that the optimum operation point of random access is achievablewith proposed learning algorithm for conditional handover.

RACH Optimization with Decision Tree Based Supervised Learning for Conditional Handover in 5G Beamformed Systems