Performance Analysis and Optimization in Massive MIMO Systems
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Date
2021-03
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Addis Ababa University
Abstract
Next generation networks are expected to support large volume of data tra c generated
from emerging applications such as ultra high speed video streaming, machine-tomachine
(M2M) communication and the Internet of things (IoT). To handle this large
volume of data tra c, these networks should employ technologies that utilize broad spectrum,
o er higher cell density and high spectral e ciency. The spectral e ciency can be
improved by increasing the transmitter power; introducing additional processing (such as
deploying multiple antenna systems and advanced modulation techniques) in transceiver
pairs that help to harvest energy; minimize multiuser-interference; and implementing innovative
wireless planning and operation strategies that save energy.
By deploying very large numbers of antennas at a base station (BS), which is called
massive multiple input multiple output (MIMO), we can signi cantly improve the spectral
e ciency of mobile networks. Besides, massive MIMO simpli es transmission processing,
improves energy e ciency and reduces the required transmission power of the users. Due
to those performance gains, massive MIMO has become an enabler for the deployment of
5G and beyond networks.
In this PhD research, we study and analyze channel modeling, resource allocation and
optimization techniques in massive MIMO systems. For this, rst we analyze recent works
on signal processing, channel modeling, channel estimation, resource allocation and optimization
techniques in massive MIMO systems. In this regard, fundamentals of massive
MIMO systems including channel capacity, spectral e ciency and energy e ciency have
been studied. Closedform lower bound expressions are derived for the spectral e ciency
and energy e ciency. Then, simulation results are provided to validate the theoretical analysis.
Besides, performance analysis is done for linear detection and precoding techniques
and then computationally e cient inverse approximation techniques are proposed for linear
detection and precoding in massive MIMO systems. Speci cally, Truncated Neumann
series-based matrix inversion approximation techniques are formulated and probability of convergence, error of approximation and computationally complexity are analyzed.
Then, we analyze achievable spectral e ciency of massive MIMO systems in realistic
propagation environment under perfect and imperfect channel state information (CSI) scenarios.
In particular, the e ects of major large scale and small fading parameters including
pathloss, shadowing, multipath fading, spatial channel correlation and impact of channel
estimation have been investigated. Spectral e ciency analysis is done for uplink massive
MIMO system under Rician fading channel model. Besides, by applying non-central to
central Wishart approximation, closedform lower bound achievable rate expressions are
formulated for massive MIMO systems in Rican fading channel model.
Then, energy e cient power control and resource allocation algorithms have been proposed.
For this, rst by using large system analysis, analytical closedform lower bound
expressions are derived for the achievable sum rate and appropriate power consumption
model is formulated for the proposed massive MIMO systems. Then, by utilizing tools from
fractional programming theory and sequential convex programming, energy e cient power
control and resource allocation algorithms have been formulated. Further, the impacts of
system and propagation parameters on energy e ciency have been evaluated. Particularly,
the impacts of maximums transmitter power and minimum rate constraints of the users
on global energy e ciency have been evaluated. The results show that the global energy
e ciency increases with the maximum transmitter power constraint and decreases with
the minimum data rate constraint.
Finally, we analyze the performance of multicell massive MIMO systems in spatially
correlated channel model. First, we study and evaluate fundamentals of multicell massive
MIMO systems. In this regard channel modeling, power allocation and spatial resource
allocation in multicell massive MIMO systems are considered. Besides, we evaluate the
impacts of spatial correlation and pilot contamination. Important trade-o s and considerations
on design and optimization of multicell massive MIMO systems has been studied.
The impacts of system and propagation parameters are evaluated theoretically and via
numerical simulation. The results show that spatial channel correlation has a major impact
on channel hardening, favorable propagation, channel estimation quality and spectral
e ciency of the system.
Description
Keywords
5G, massive MIMO, spectral effciency, energy effciency, optimization, resource allocation