Interference Management In Spectrally and Energy Efficient Wireless Networks / Mohamed Seif Eldin Mohamed Abdelmoneim Mohamed

Supervisor: Mohamed Nafie

Thesis (M.A.)—Nile University, Egypt, 2016 .

"Includes bibliographical references"

Contents:
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Achievable Degrees of Freedom of the K-user MISO Broadcast Channel
with Alternating CSIT via Interference Creation-Resurrection . . . . . . 4
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Proposed Interference Creation-Resurrection Scheme . . . . . . . . 11
2.3.1 Phase 1: Interference Creation . . . . . . . . . . . . . . 12
2.3.2 Phase 2: Interference Resurrection . . . . . . . . . . . . 13
2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3. Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks
via Binary Consensus Algorithms . . . . . . . . . . . . . . . . . . . . . . 23
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
x
3.2.1 Compressive Sensing . . . . . . . . . . . . . . . . . . . . . . 25
3.2.2 Binary Consensus Algorithm . . . . . . . . . . . . . . . . . 27
3.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.4 Proposed Sensing Scheme . . . . . . . . . . . . . . . . . . . . . . . 33
3.5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4. Cooperative D2D Communication in Downlink Cellular Networks with
Energy Harvesting Constraints . . . . . . . . . . . . . . . . . . . . . . . 42
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.2.1 Direct Transmission Scheme . . . . . . . . . . . . . . . . . . 47
4.2.2 Cooperative Transmission Scheme . . . . . . . . . . . . . . 47
4.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5. Sparse Signal Processing Concepts for Efficient 5G System Design . . . . 59
5.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.2 Enabling 5G Technical Concepts . . . . . . . . . . . . . . . . . . . 60
5.3 Joint Activity and Data Detection for Machine to Machine Communication
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.4 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.5 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.6 Proposed Solutions for Activity and Data Detection . . . . . . . . 64
5.6.1 Generalized Likelihood Ratio Test . . . . . . . . . . . . . . 64
5.6.2 MMSE Activity Recovery . . . . . . . . . . . . . . . . . . . 65
6. Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 67
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
6.2 Future Work: Joint Activity and Data Detection for Machine to
Machine Communication . . . . . . . . . . . . . . . . . . . . . . . . 68
6.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
6.4 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.4.1 Proposed Solutions for Activity and Data Detection . . . . 71
6.4.2 Generalized Likelihood Ratio Test . . . . . . . . . . . . . . 71
6.4.3 MMSE Activity Recovery . . . . . . . . . . . . . . . . . . . 71
Bibliography . . . . . . . . . . . . . . .

Abstract:
In this thesis, we explore different trends in the design of wireless networks. The
first work of this thesis, we investigate on the interference management problem with
limited channel state information in wireless network specifically at the transmitter(s).
Channel state information at the transmitter affects the degrees of freedom of the
wireless networks. In this paper, we analyze the DoF for the K-user multiple-input
single-output (MISO) broadcast channel (BC) with synergistic alternating channel
state information at the transmitter (CSIT). Specifically, the CSIT of each user alternates
between three states, namely, perfect CSIT (P), delayed CSIT (D) and no
CSIT (N) among different time slots. For the K-user MISO BC, we show that the
total achievable degrees of freedom (DoF) are given by K2
2K−1 through utilizing the
synergistic benefits of CSIT patterns. We compare the achievable DoF with results
reported previously in the literature in the case of delayed CSIT and hybrid CSIT
models.
Secondly, Compressive Sensing (CS) is utilized in Cognitive Radio Networks (CRNs)
to exploit the sparse nature of the occupation of the primary users. Also, distributed
spectrum sensing has been proposed to tackle the wireless channel problems, like
node or link failures, rather than the common “centralized approach” for spectrum
sensing. In this work, we propose a distributed spectrum sensing framework based on
consensus algorithms where SU nodes exchange their binary decisions to take global
decisions without a fusion center to coordinate the sensing process. Each SU will
share its decision with its neighbors, and at every new iteration each SU will take a
new decision based on its current decision and the decisions it receives from its neighbors;
in the next iteration, each SU will share its new decision with its neighbors. We
show via simulations that the detection performance can tend to the performance of
majority-rule Fusion Center based CRNs.
As a solution for the spectrum shrinkage, Device-to-Device (D2D) communications
have been highlighted as one of the promising solutions to enhance spectrum
utilization of LTE-Advanced networks. In this work, we consider a D2D transmitter
cooperating with a cellular network by acting as a relay serve one of the cellular user
equipments. We consider the case in which the D2D transmitter is equipped with
an energy harvesting capability. We investigate the tradeoff between the amount of
energy used for relaying and the energy used for decoding the cellular user data. We
formulate an optimization problem to maximize the cellular user rate subject to a
minimum rate requirement constraint for the D2D link. Finally, we show via numerical
simulations the benefits of our cooperation-based system as compared to the
non-cooperative scenario.
It is inevitable that, 4G will not be able to the user demands that is the data traffic
is growing exponentially. Due to the advent of Compressive Sensing (CS), methods
that can optimally exploit sparsity in signals that will be the key enabler in the design
of 5G systems. We give a glimpse on the future design aspects in 5G communications
systems. Besides that, a new type of communication system called Machine-to-
Machine (M2M) communications that will be involved with a salient portion of the
5G data traffic is highlighted. We study the problem of multi-user detection (MUD)
in M2M communications utilizing the tool of CS, also, proposing different recovery
techniques with the aid of multiple antennas techniques.

Text in English, abstracts in English.