COMPARISON OF LINEAR CHANNEL ESTIMATION TECHNIQUES FOR 5G NETWORKS

Abstract

We are observing a revolution in wireless technology, where the society is demanding new services, such as smart cities, autonomous vehicles, augmented reality, etc. These challenging services not only are demanding a vast increase of data rates in the range of 1000 times higher, but also they are real-time applications with an important delay constraint. Furthermore, an extraordinary number of different machine-type devices will be connected to the network, known as Internet of Things (IoT), where they will be transmitting real-time measurements from different sensors. In this context, the Third Generation Partnership Project (3GPP) has already developed the new Fifth Generation (5G) of mobile communication systems, which should be capable of satisfying all the requirements. Hence, 5G will provide three key aspects, such as: enhanced mobile broad-band (eMBB) services, massive Area of interest in this work focus on transmitter and receiver RF propagation Channel estimation best techniques impact analysis with respect to achievable sum rates in Massive MIMO systems. In addition to study the massive MIMO RF propagation channels estimation system, the interested in the Channel estimation among different type techniques: Minimum Mean Square Error (MMSE), Zero Forcing (ZF) and Maximum Ratio Transmission (MRT) precoding. Theoretically, the precoding is known as Space Division Multiple Access. Each linear precoding shows the best performance with each signal power regime. For the comparison between MRT and ZF, MRT gives better performance at low signal to noise ratio (SNR) while ZF performs better at high SNR. MMSE gives the best channel estimation across the entire SNR.