Date of Award


Document Type


Degree Name


Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering

First Advisor

Mohammad A. Matin, Ph.D.

Second Advisor

Ronald DeLyser

Third Advisor

Yun Bo Yi

Fourth Advisor

Vijaya Narapareddy


Base station, Channel state information, Massive MIMO, Multiple input multiple output, Signal to noise ratio, Spatial correlation, Wireless communication


During the past few years, the number of wireless devices has been increasing rapidly. Wireless networks are serving and connecting billions of wireless devices where these devices are demanding higher data rate and lower latency to be able to support voice, video and gaming applications. Moreover, the consumed energy by the wireless systems will be increasing. Hence, the Fifth generation (5G) wireless networks needs to provide higher data rate, serve larger number of users simultaneously and be more energy efficient. One of the promising technologies that can meet the above requirements is Massive Multiple Input Multiple Output (MIMO). The main concept of this technology is to equip the base station with hundreds of antennas and serve tens of users simultaneously. The amount of research on massive MIMO increases rapidly, but there is little attention so far on the spatial correlation between the channels. Most of the published work are assuming that the antennas are uncorrelated which is not the case in real-world situations. In this dissertation, the effect of channel correlation model on the Massive MIMO performance is investigated.

First, the exponential correlation model is applied to the Massive MIMO system model. We used a pilot based linear minimum mean square error (LMMSE) channel estimator for the uplink data transmission. The impact of the channel correlation on the channel estimation accuracy is investigated. Due to having channel reciprocity, the channel state information will be the same for uplink and downlink data transmission. It is assumed that there is block fading where there are static channels. It is shown that the channel estimation is more accurate with higher SNR values.

Second, the uplink and downlink spectral efficiency of the LMMSE estimators are investigated where spatial correlation models are applied to the system to generate the channel covariance matrix. The lower capacity of the uplink and downlink data transmissions are derived to see the effect of applying exponential correlation model. We study the lower capacity bound based on imperfect knowledge of the channel. In the first part, we are considering a one cell system model with one base station that is equipped with N antennas and serving single antenna user. In the second part, a Massive MIMO system of a single cell is considered. The system model is having a base station with multiple antennas that is serving user terminals equipped with multiple antennas. It is proved that the spectral efficiency is improved by increasing the number of base station antennas which shows the scalability of Massive MIMO systems.

Finally, the transmit power of Massive MIMO system is defined as the consumed energy by the amplifier divided by coherence time while energy efficiency of Massive MIMO system can be expressed as the ratio between the spectral efficiency and the emitted power. The influence of the channel spatial correlation on the energy efficiency is investigated where it is noticed that there is higher energy efficiency with higher number of base station antennas

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Saleh Albdran


Received from ProQuest

File Format




File Size

126 p.


Electrical engineering