الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
In future cellular 5G, the network operators have recognized the spectrum scarcity as one
of the major problem. Therefore, expanding or even re-utilizing the current bandwidth
requires an advanced techniques to accommodate the enormous communicating devices.
In this thesis, Energy Effciency (EE) is jointly carried out with respect to a multivariables of Base Station (BS) density, user number per cell, number of attached antennas,
and power control coeffcient. Although deploying dense small cells provides high EE, the
network performance is still limited due to accumulated both user and inter-cell interference.
As a result, we investigate the reasons of mutual selection among the optimal design
parameters where, they contribute in mitigating the accumulated interference. For ease
and simplicity of the solution, we develop a lower bound of the EE. Accordingly, we can
provide a deep insight of performance through interplay among the optimization parameters.
Moreover, the impact of hardware distortion on the optimized EE will be investigated.
Further, the channel estimation is analytically introduced. Moreover, the results will be
extended to study the energy consumption in terms of the selected optimization variables.
In the fact, EE consists of two conflicting objectives that are recognized as beneft-cost
ratio: Spectral Effciency (SE) and energy consumption. As a result, multi-objective
optimization of contradicting EE objectives are studied to jointly maximize network
SE while minimizing the power consuming with respect to the following operational
parameters: Base Station density, Users numbers, equipped BS antennas number, signalto-noise power ratio. As well, the performance of ultra dense network is characterized on
basis of Pareto optimally concept through the following benchmarks: (1) Studying the
impact of exhausted power at different parts of deployed hardware elements. (2) Validating
the total EE performance through Monte Carlo simulation and proposed evolutionary
multi-objective optimization at low cost of processing time. (3) The proposed approach is
compared against single objective scheme to show the signifcance of design trade-off.
Hybrid precoding techniques could be introduced as cost-effective solution for mmWave
massive Multi-Input Multi-Output MIMO scenario. We simply design the digital precoding
stage based on modifed water-flling wherein, the orthogonality criterion among transmitted data streams is guaranteed. Besides, the analog beamformers are optimally designed
via extracting steering angles from an alternative precoder which is derived from a tight and simple upper bound expression. Furthermore, the proposed algorithm will be extended
to include the practical analog beamformers that have limited phase shifter with fnite
angle resolution. As a result, we will develop a quantization technique, for practical
analog precoder, with precision up to two bits. Furthermore, we investigate the EE and SE
performances against the state-of-art.
Non-Orthogonal Multiple Access (NOMA) has been integrated with beamspace MIMO
to enhance system SE by serving more than one user per dedicated RF chain. However,
balancing the system performance with further RF reduction is still challenging. Accordingly, we propose beamspace MIMO-NOMA system based on Signal Alignment (SA)
concept, where we will be able to further reduce hardware complexity and introduce a
signifcant performance through: (1), the excess Degrees-of-Freedom (DoF) of beamspace
MIMO-NOMA are exploited by SA to suppress the inter-beam/pair interference via designing detection and precoding matrices. (2), power allocation coeffcients are evaluated
upon to the optimal SE in order to mitigate intra-beam/pair interference. Moreover, we
derive tight closed formula for optimal SE based on Karuch-Kuhn-Tucker (KKT) analysis
which is validated by heuristic-based genetic solution. Then, the performance is compared
against Orthogonal Multiple Access (OMA) technique.