mobility-management

Deep learning-based 5G handover prediction using real drive-test measurements, RSRP time-series forecasting, and LSTM/GRU/TCN models for predictive mobility management.

KTX 고속철도 3GPP NR/LTE 핸드오버 시뮬레이터 — field GPS 궤적으로 A3/A5/B1/B2 HO·RLF·HOF 재현 (statistical + Sionna RT 레이트레이싱)

Simulation code for a risk-aware conditional handover timing policy in 5G NR LEO non-terrestrial networks, using a lower-confidence-bound on predicted remaining usable link time under uncertainty. Evaluated via Monte Carlo simulation against fixed-guard-time, point-prediction, and quality-threshold baselines. Target: ISETC'26.

Predictive mobility management in LTE/O-RAN-like ns-3 scenarios with candidate-aware LSTM, MLP baseline, replay and online closed-loop validation.