Partially Blind Handovers for mmWave New Radio Aided by Sub-6 GHz LTE Signaling

TL;DR

This paper introduces a machine learning-based approach for partially blind handovers between sub-6 GHz LTE and mmWave bands, significantly improving success rates in colocated cellular networks.

Contribution

It proposes the concept of partially blind handovers and applies machine learning to predict handover success, enhancing existing methods in realistic network scenarios.

Findings

Improved handover success rates with the proposed algorithm

Effective use of sub-6 GHz and mmWave channel measurements

Validation through simulation in colocated cell setups

Abstract

For a base station that supports cellular communications in sub-6 GHz LTE and millimeter (mmWave) bands, we propose a supervised machine learning algorithm to improve the success rate in the handover between the two radio frequencies using sub-6 GHz and mmWave prior channel measurements within a temporal window. The main contributions of our paper are to 1) introduce partially blind handovers, 2) employ machine learning to perform handover success predictions from sub-6 GHz to mmWave frequencies, and 3) show that this machine learning based algorithm combined with partially blind handovers can improve the handover success rate in a realistic network setup of colocated cells. Simulation results show improvement in handover success rates for our proposed algorithm compared to standard handover algorithms.