# Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems
## 1 Introduction  
Communication in wireless channels is affected by multipath fading which causes a random fluctuation in the received signal power. This random fluctuation in signal level is known as fading, which affects the quality and reliability of wireless communication. Therefore, the need for a high data rate and high reliability is extremely challenging. One of the key technologies to increase network throughput by spatially exploiting more spectrum is achieved with MIMO technology. The leverages of MIMO are achieved through utilizing the spatial dimensions (submitted by the multiple antennas at the transmitter and the receiver) [1], where it was first investigated in point to point MIMO or single user MIMO which is called (SU-MIMO). However, the conventional MIMO system needs more effort to exploit the spatial multiplexing gains due to signal levels are exposed to the attenuation at cell edges as compared to interference levels there. A substitute for a point-to-point MIMO system is a multiuser MIMO system (MU-MIMO) in which an antenna array simultaneously serves multipleterminals independently. Then, the multiplexing throughput gains are shared among these terminals under line-of-sight propagation conditions, multiplexing gains can disappear for point-to-point MIMO but are retained in the multi-user system provided the angular separation of the users beyond the Rayleigh resolution of the array [2]. When the base station is equipped with a hundred antennas or more, better and more reliable communication is achieved. With very large MIMO system \"Massive MIMO\", offers excess degrees of freedom that can be used to make sure that noise, fading and hardware imperfections vanished when signals from a large number of antennas are collected in the air jointly. The same property that makes massive MIMO flexible against fading also makes the technology very resistant to failure of one or a few of the antenna units. Massive MIMO can increase the capacity 10 times or more, can improve the radiated energy efficiency in order of 100 times, and can improve the quality of the received signal and achieve the link reliability [3,4]. The complexity scale in a conventional MIMO system concentrates at the uplink side because the main challenge of the conventional MIMO receiver is how to eliminate the multi-stream interference (MSI) since there are multiple streams from the individual transmit antennas interfere with each other at the receiver side. The multi-stream signal can be detected in a conventional MIMO system using linear equalization schemes such as Zero-Forcing (ZF) and Minimum Mean Squared Error (MMSE) which they are preferred for their less complexity scale; however, these schemes tend to suffer from poor bit error rate (BER) performance. While the nonlinear and the optimal receiver scheme is the Maximum likelihood (ML) detector, which exhibits higher performance, but the high computational complexity of this detector has made it inconsistent for widespread adoption in practical MIMO receiver designs when higher order constellations and a large number of antennas are used [5,6]. On the other hand, the complexity scale in massive MIMO system depends on which duplex system is used for the communication channel. When massive MIMO uses TDD principle, both uplink and downlink are used the same frequency spectrum but different time slots. At the uplink side, \(K\)-users send \(K\)-orthogonal pilot sequences to the base station for estimating the channels, while at the downlink side, using the concept of channel reciprocity base station utilizes the estimated channel at the uplink side to precode the transmit symbols. Hence, the complexity scale for the training process needs a minimum of \(2K\) channel uses. However, when the massive system uses FDD principle, \(K\)-users transmit \(K\)-orthogonal pilot sequences to the base station for estimating the channels.