MARC details
| 000 -LEADER |
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| fixed length control field |
08044nam a22002537a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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210223b2016 a|||f mb|| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
EG-CaNU |
| Transcribing agency |
EG-CaNU |
| 041 0# - Language Code |
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| Language code of text |
eng |
| Language code of abstract |
eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
005 |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Mohamed Seif Eldin Mohamed Abdelmoneim Mohamed |
| 245 1# - TITLE STATEMENT |
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| Title |
Interference Management In Spectrally and Energy Efficient Wireless Networks / |
| Statement of responsibility, etc. |
Mohamed Seif Eldin Mohamed Abdelmoneim Mohamed |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Date of publication, distribution, etc. |
2016 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
96 p. |
| Other physical details |
ill. |
| Dimensions |
21 cm. |
| 500 ## - GENERAL NOTE |
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| Materials specified |
Supervisor: Mohamed Nafie |
| 502 ## - Dissertation Note |
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| Dissertation type |
Thesis (M.A.)—Nile University, Egypt, 2016 . |
| 504 ## - Bibliography |
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| Bibliography |
"Includes bibliographical references" |
| 505 0# - Contents |
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| Formatted contents note |
Contents:<br/>1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1<br/>1.1 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 3<br/>2. Achievable Degrees of Freedom of the K-user MISO Broadcast Channel<br/>with Alternating CSIT via Interference Creation-Resurrection . . . . . . 4<br/>2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4<br/>2.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8<br/>2.3 Proposed Interference Creation-Resurrection Scheme . . . . . . . . 11<br/>2.3.1 Phase 1: Interference Creation . . . . . . . . . . . . . . 12<br/>2.3.2 Phase 2: Interference Resurrection . . . . . . . . . . . . 13<br/>2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16<br/>3. Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks<br/>via Binary Consensus Algorithms . . . . . . . . . . . . . . . . . . . . . . 23<br/>3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23<br/>3.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25<br/>x<br/>3.2.1 Compressive Sensing . . . . . . . . . . . . . . . . . . . . . . 25<br/>3.2.2 Binary Consensus Algorithm . . . . . . . . . . . . . . . . . 27<br/>3.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31<br/>3.4 Proposed Sensing Scheme . . . . . . . . . . . . . . . . . . . . . . . 33<br/>3.5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 36<br/>4. Cooperative D2D Communication in Downlink Cellular Networks with<br/>Energy Harvesting Constraints . . . . . . . . . . . . . . . . . . . . . . . 42<br/>4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42<br/>4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46<br/>4.2.1 Direct Transmission Scheme . . . . . . . . . . . . . . . . . . 47<br/>4.2.2 Cooperative Transmission Scheme . . . . . . . . . . . . . . 47<br/>4.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 49<br/>4.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 54<br/>5. Sparse Signal Processing Concepts for Efficient 5G System Design . . . . 59<br/>5.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59<br/>5.2 Enabling 5G Technical Concepts . . . . . . . . . . . . . . . . . . . 60<br/>5.3 Joint Activity and Data Detection for Machine to Machine Communication<br/>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62<br/>5.4 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62<br/>5.5 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63<br/>5.6 Proposed Solutions for Activity and Data Detection . . . . . . . . 64<br/>5.6.1 Generalized Likelihood Ratio Test . . . . . . . . . . . . . . 64<br/>5.6.2 MMSE Activity Recovery . . . . . . . . . . . . . . . . . . . 65<br/>6. Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 67<br/>6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67<br/>6.2 Future Work: Joint Activity and Data Detection for Machine to<br/>Machine Communication . . . . . . . . . . . . . . . . . . . . . . . . 68<br/>6.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68<br/>6.4 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69<br/>6.4.1 Proposed Solutions for Activity and Data Detection . . . . 71<br/>6.4.2 Generalized Likelihood Ratio Test . . . . . . . . . . . . . . 71<br/>6.4.3 MMSE Activity Recovery . . . . . . . . . . . . . . . . . . . 71<br/>Bibliography . . . . . . . . . . . . . . . |
| 520 3# - Abstract |
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| Abstract |
Abstract:<br/>In this thesis, we explore different trends in the design of wireless networks. The<br/>first work of this thesis, we investigate on the interference management problem with<br/>limited channel state information in wireless network specifically at the transmitter(s).<br/>Channel state information at the transmitter affects the degrees of freedom of the<br/>wireless networks. In this paper, we analyze the DoF for the K-user multiple-input<br/>single-output (MISO) broadcast channel (BC) with synergistic alternating channel<br/>state information at the transmitter (CSIT). Specifically, the CSIT of each user alternates<br/>between three states, namely, perfect CSIT (P), delayed CSIT (D) and no<br/>CSIT (N) among different time slots. For the K-user MISO BC, we show that the<br/>total achievable degrees of freedom (DoF) are given by K2<br/>2K−1 through utilizing the<br/>synergistic benefits of CSIT patterns. We compare the achievable DoF with results<br/>reported previously in the literature in the case of delayed CSIT and hybrid CSIT<br/>models.<br/>Secondly, Compressive Sensing (CS) is utilized in Cognitive Radio Networks (CRNs)<br/>to exploit the sparse nature of the occupation of the primary users. Also, distributed<br/>spectrum sensing has been proposed to tackle the wireless channel problems, like<br/>node or link failures, rather than the common “centralized approach” for spectrum<br/>sensing. In this work, we propose a distributed spectrum sensing framework based on<br/>consensus algorithms where SU nodes exchange their binary decisions to take global<br/>decisions without a fusion center to coordinate the sensing process. Each SU will<br/>share its decision with its neighbors, and at every new iteration each SU will take a<br/>new decision based on its current decision and the decisions it receives from its neighbors;<br/>in the next iteration, each SU will share its new decision with its neighbors. We<br/>show via simulations that the detection performance can tend to the performance of<br/>majority-rule Fusion Center based CRNs.<br/>As a solution for the spectrum shrinkage, Device-to-Device (D2D) communications<br/>have been highlighted as one of the promising solutions to enhance spectrum<br/>utilization of LTE-Advanced networks. In this work, we consider a D2D transmitter<br/>cooperating with a cellular network by acting as a relay serve one of the cellular user<br/>equipments. We consider the case in which the D2D transmitter is equipped with<br/>an energy harvesting capability. We investigate the tradeoff between the amount of<br/>energy used for relaying and the energy used for decoding the cellular user data. We<br/>formulate an optimization problem to maximize the cellular user rate subject to a<br/>minimum rate requirement constraint for the D2D link. Finally, we show via numerical<br/>simulations the benefits of our cooperation-based system as compared to the<br/>non-cooperative scenario.<br/>It is inevitable that, 4G will not be able to the user demands that is the data traffic<br/>is growing exponentially. Due to the advent of Compressive Sensing (CS), methods<br/>that can optimally exploit sparsity in signals that will be the key enabler in the design<br/>of 5G systems. We give a glimpse on the future design aspects in 5G communications<br/>systems. Besides that, a new type of communication system called Machine-to-<br/>Machine (M2M) communications that will be involved with a salient portion of the<br/>5G data traffic is highlighted. We study the problem of multi-user detection (MUD)<br/>in M2M communications utilizing the tool of CS, also, proposing different recovery<br/>techniques with the aid of multiple antennas techniques. |
| 546 ## - Language Note |
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| Language Note |
Text in English, abstracts in English. |
| 650 #4 - Subject |
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| Subject |
Wireless Technologies |
| 655 #7 - Index Term-Genre/Form |
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| Source of term |
NULIB |
| focus term |
Dissertation, Academic |
| 690 ## - Subject |
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| School |
Wireless Technologies |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| 650 #4 - Subject |
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| -- |
327 |
| 655 #7 - Index Term-Genre/Form |
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| -- |
187 |
| 690 ## - Subject |
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| -- |
327 |