Uplink Channel Impulse Response Based Secondary Carrier Prediction

TL;DR

This paper proposes a machine learning approach to predict secondary carriers for inter-frequency handover using uplink reference signals, aiming to improve energy efficiency in dense future networks.

Contribution

It introduces a novel ML-based method for secondary carrier prediction leveraging uplink signals, reducing signaling complexity and energy consumption during handovers.

Findings

Potential reduction in signaling overhead

Improved energy efficiency for user equipment

Enhanced handover decision accuracy

Abstract

A typical handover problem requires sequence of complex signaling between a UE, the serving, and target base station. In many handover problems the down link based measurements are transferred from a user equipment to a serving base station and the decision on handover is made on these measurements. These measurements together with the signaling between the user equipment and the serving base station is computationally expensive and can potentially drain user equipment battery. Coupled with this, the future networks are densely deployed with multiple frequency layers, rendering current handover mechanisms sub-optimal, necessitating newer methods that can improve energy efficiency. In this study, we will investigate a ML based approach towards secondary carrier prediction for inter-frequency handover using the up-link reference signals.