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.
翻译:典型的移交问题要求UE、服务基地站和目标基地站之间一系列复杂的信号。在许多移交问题中,基于下链路的测量从用户设备转移到服务基地站,并就这些测量做出移交决定。这些测量与用户设备和服务基地站之间的信号一起,计算成本高昂,有可能耗尽用户设备电池。与此相加,未来网络以多频层密集部署,使目前的移交机制变得不那么理想,需要更新方法来提高能效。在本研究中,我们将调查基于ML的二级承运人使用上链路参照信号预测频率间传输的方法。