Estudo do comportamento de canal em redes 5G: uma análise preditiva

Abstract

A paramether that begins to be particularly important on the dimensionament of the 5G networks is the communication channel state, mainly considering the new frequencies used, the growing number of connected devices and the necessity of allocate resources efficiently. To dimension the channel effects, the Base Station (BS) receives from the User Equipment (UE) a measurement of the channel quality, called Channel Quality Information/Indicator (CQI), from wich makes the scheduling of the resources to better attend the users. The study developed here wants to generate a reliable data base of a CQI flow along a transmission into a mobile network Long Term Evolution Advanced (LTEA) with some parameters of the Release 15 of the Technical Specification (TS) from 3rd Generation Partnership Project (3GPP) of 5G using Matlab. After this, it is purposed the aplication of a temporal neural network to predict the CQI along a transmission. Lastly, evaluates the reability of the network, comparing the predicted values by the system and the real ones.