# Early-Scheduled Handover Preparation in 5G NR Millimeter-Wave Systems

arXiv: https://arxiv.org/abs/2411.09720 · HTML: https://mlsanity.com/paper/2411.09720/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 14 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Probabilistic Forecasting with Temporal Convolutional Neural Network](https://mlsanity.com/paper/1906.04397/) (2019)
- [A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies](https://mlsanity.com/paper/1804.01908/) (2018)
- [Analysis of Handover Failures in Heterogeneous Networks with Fading](https://mlsanity.com/paper/1507.01586/) (2015)
- [On the Modeling and Analysis of Fast Conditional Handover for 5G-Advanced](https://mlsanity.com/paper/2204.06909/) (2022)
- [Mobility Management in 5G and Beyond: A Novel Smart Handover with Adaptive Time-to-Trigger and Hysteresis Margin](https://mlsanity.com/paper/2207.01706/) (2022)
- [Data Driven Optimization of Inter-Frequency Mobility Parameters for Emerging Multi-band Networks](https://mlsanity.com/paper/2008.08200/) (2020)

## Abstract (arXiv)

The handover (HO) procedure is one of the most critical functions in a cellular network driven by measurements of the user channel of the serving and neighboring cells. The success rate of the entire HO procedure is significantly affected by the preparation stage. As massive Multiple-Input Multiple-Output (MIMO) systems with large antenna arrays allow resolving finer details of channel behavior, we investigate how machine learning can be applied to time series data of beam measurements in the Fifth Generation (5G) New Radio (NR) system to improve the HO procedure. This paper introduces the Early-Scheduled Handover Preparation scheme designed to enhance the robustness and efficiency of the HO procedure, particularly in scenarios involving high mobility and dense small cell deployments. Early-Scheduled Handover Preparation focuses on optimizing the timing of the HO preparation phase by leveraging machine learning techniques to predict the earliest possible trigger points for HO events. We identify a new early trigger for HO preparation and demonstrate how it can beneficially reduce the required time for HO execution reducing channel quality degradation. These insights enable a new HO preparation scheme that offers a novel, user-aware, and proactive HO decision making in MIMO scenarios incorporating mobility.