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Characteristics of Radio Propagation Channels
The radio propagation of a message over a wireless channel is aected by several sources of perturbation. Channels are time and frequency varying. These variations are due to the nature of the medium of transmission (water, air, space, wood, brick, etc.) as well as the conguration of devices, their location in the environment and the specic properties of the environments (presence of walls, mobility of users, fading, shadowing, scattering, an so forth). We detail hereafter some causes of radio degradation we have considered in our numerical simulations.
Preliminary on Femtocells Networks
The multiplicity of wireless communication systems increases the spectrum pollution due to interference. Networks operators aim hence at getting more reliable and robust wireless links between network access points and customers. An eective way for them to increase system capacity is by getting the transmitter and receiver closer to each other, which creates the dual benets of higher-quality links and more spatial reuse. Indeed, close receivers are less aected by prejudicial events occurring during radio propagation. Besides, transmit power can be lowered, resulting in lower in-band interference. With nomadic users which sporadically access to the network, shorten distance inevitably involves deploying more infrastructure, typically in the form of microcells, hot spots, distributed antennas, or relays. In other words, numerous short-range low-power access points are deployed instead of wide-range high-power BSs. Recently, the concept of femtocells (FC) – also called home base stations (H-BS) or femto access point (FAP) – has been proposed and adopted as a less expensive alternative. Femtocells networks consist in equipping with FAPs local indoor or outdoor areas, such as home, oce, mall, park, to get better coverage [27, 59, 60].
In practice, with the interoperability of standards and systems, femtocells networks can be shown as LANs, where H-BSs replace the common ‘boxes’ of network operators (Internet, VoIP, television DVB). Besides, H-BSs consist also in a gateway through the 3G/4G network. Advantages of such deployments are many-fold.
H-BSs ensure local (short-range) coverage with reduced transmit power and so lower power budget.
In-band interference between femtocells is such a way reduced.
Preliminary on Interference Mitigation Techniques
In this section we present a selection of the most eective techniques to cope with the interference issue. The book of Tse and Viswanath [2] provides denitely useful basis to understand this issue. Each interference mitigation technique can be applied individually or combined with other techniques to achieve better performance. In most cases, a single technique cannot perfectly cancel interference alone. Furthermore, important tasks are done by upper layers, such as scheduling, which plays an important role in resource management to guarantee QoS and fairness between customers ; but such aspects are not addressed here.
Several approaches have been proposed in the literature to cope with in-band interference in dense communication systems. Interference aware techniques can be either static or dynamic. First, static techniques design a global and xed solution to avoid catastrophic instances of in-band interference. On the other hand, dynamic techniques update periodically transmission parameters to take time, frequency, space variations of the communica tion context into account. The topology of the network (mobility of users with sporadic access to network) and customers requirements (possibly dierent between devices, varying with time of the day, location, etc.) are other fundamental system parameters. With interference management, three axes can be distinguished. First, an easy way to deal with interference is to avoid creating it : interference avoidance or interference coordination techniques allocate and manage resources so that interference is not generated.
Such techniques are performed prior to any transmission and assume commonly CSIT knowledge. Second, interference cancellation techniques are performed to suppress the perceived interference at destination. They mainly need CSIR knowledge. Finally, interference randomization techniques are less frequent but consist in spreading randomly users’ transmissions over sets of sub-carriers so as to randomize the perceived interference and achieve frequency diversity gain [66]. However, another classication is adopted in the remainder to introduce interference mitigation techniques. We rather consider four topics, namely orthogonal resource allocation, resources sharing, information theory and signal processing, and lastly channel aware power control.
Table of contents :
Remerciements
Abstract
Résumé long
Contents
List of Figures
List of Tables
Nomenclature
Abbreviations and Acronyms
Thesis Specic Notations
1 Snapshot on the Thesis
1.1 Background and Motivations
1.2 Thesis Objectives
1.3 Thesis Outline
2 Fundamentals of Interference in Wireless Networks
2.1 Introduction
2.1.1 Technical Context
2.1.2 Motivations
2.1.3 Examples of Potential Industrial Applications
2.2 Preliminary on Wireless Communications
2.2.1 Classical System Models
2.2.1.1 Broadcast Channel
2.2.1.2 Multiple Access Channel
2.2.1.3 Interference Channel
2.2.1.4 X Channel and Z Channel
2.2.2 Notions, Concepts and Denitions
2.2.3 PhD Assumptions
2.2.4 Characteristics of Radio Propagation Channels
2.2.5 PhD Hints of Research
2.3 Preliminary on Femtocells Networks
2.4 Preliminary on Interference Mitigation Techniques
2.4.1 Orthogonal Resource Allocation
2.4.2 Non-Orthogonal Resource Allocation
2.4.2.1 Frequency Reuse
2.4.2.2 Divide and Conquer : Graph Colouring
2.4.2.3 Cognitive Radio Approach
2.4.3 Information Theory and Signal Processing Techniques
2.4.3.1 Noisy Strategy
2.4.3.2 Zero Forcing
2.4.3.3 ML, MMSE Estimation, Sphere Decoder
2.4.3.4 Dirty Paper Coding
2.4.3.5 Successive Interference Cancellation
2.4.3.6 Beamforming
2.4.3.7 Interference Alignment
2.4.4 Channel Aware Power Control
2.4.4.1 Binary Power Allocation
2.4.4.2 Water-Filling
2.5 Conclusions
3 Cooperative Communications with Half-Duplex Relays
3.1 Introduction
3.1.1 Motivations
3.1.2 Contributions
3.1.3 Related Works
3.2 System Model and Assumptions
3.2.1 The Two-Hop Half-Duplex Relay Channel
3.2.2 The Global System Model
3.3 Preliminary on Cooperative Communications
3.3.1 Cooperative Transmissions’Goals
3.3.2 State of the Art on Cooperative Protocols
3.3.2.1 Orthogonal, Non-Orthogonal and Slotted Protocols
3.3.2.2 Amplify-and-Forward
3.3.2.3 Decode-and-Forward
3.3.2.4 Compress, Equalize and Hybrid Protocols
3.3.3 Cooperative Trade-O : Robustness vs. Interference
3.3.4 Distributed Coding Techniques
3.4 Radio Resource Management for Interference Mitigation
3.4.1 Cooperative Communication Paradigm
3.4.2 Description of Resource Allocation Patterns
3.4.2.1 ‘Classic 1’ Family of Allocation Patterns (C1)
3.4.2.2 ‘Classic 2’ Family of Allocation Patterns (C2)
3.4.2.3 Proposed ‘Advanced’ Family of Allocation Patterns (A)
3.4.2.4 Other Possible Allocation Patterns
3.4.3 Adaptive Resource Allocation Process (ARAP)
3.4.4 Simulation Results
3.4.4.1 First Scenario : Unidirectional moving along ‘s1-r1 axis
3.4.4.2 Second Scenario : Omnidirectional moving within sector
3.5 Generalization to Multi-Chunks Allocations
3.6 Conclusions
4 Adaptive Interference Handling Techniques
4.1 Introduction
4.1.1 Motivations
4.1.2 Contributions
4.1.3 Related Works
4.2 Preliminary on Adaptive Interference Handling
4.2.1 Noisy Strategy : Treat Interference As Noise
4.2.2 SIC-based Strategies
4.2.3 Time/Frequency Sharing
4.2.4 How Adaptation Can Be Achieved ?
4.2.4.1 Han and Kobayashi Model : Superposition Coding
4.2.4.2 Diversity Multiplexing Trade-O and Interference Classier
4.3 Proposed Slim Three-Regime Interference Classication
4.3.1 The Noisy Regime
4.3.2 The Joint Decoding Regime
4.3.3 The Very Strong Regime
4.3.4 Performance of Three-Regime Classier
4.3.5 Achievable SNR Region
4.4 Three-Regime Classier for n-user Interference Channel
4.4.1 Generalization of the System Model
4.4.2 Regimes Boundaries and SNR-Achievable Region
4.4.3 Outage Probability Formulations
4.4.4 Remarks
4.5 Conclusions
5 Centralized Power Allocation Algorithm
5.1 Introduction
5.1.1 Motivations
5.1.2 Contributions
5.1.3 Related Works
5.2 System Model and Assumptions
5.3 Preliminary on Centralized Algorithms
5.3.1 Challenges and Assumptions
5.3.2 Limitations of Centralized Algorithms
5.3.3 Water-Filling Technique
5.4 Proposed Centralized Power Allocation Algorithm
5.4.1 Power Allocation and Interference Classication
5.4.2 Optimal Power Allocation
5.4.3 Proof of Existence
5.4.4 Proof of Optimality
5.4.5 Simulation Results and Remarks
5.5 CPA Generalization to Multi-Source Multi-Destination Cases
5.5.1 System Model and Notations
5.5.2 Mathematical Formulation
5.6 Conclusions
6 Distributed Power Allocation Algorithm
6.1 Introduction
6.1.1 Motivations
6.1.2 Contributions
6.1.3 Related Works
6.2 System Model and Assumptions
6.3 Preliminary on Distributed Approaches
6.3.1 Benets of Distributed Allocation Algorithms
6.3.2 Limitations of Distributed Allocation Algorithms
6.3.3 A Step Towards Game Theory
6.3.4 Distributed Norm and Convergence Criterion
6.3.5 Centralized vs. Distributed Algorithms
6.4 Proposed Distributed Power Allocation Algorithm
6.4.1 Optimal Distributed Power Allocation
6.4.2 The ‘Ping-Pong’ Iterative Process
6.4.3 Proof of Convergence
6.4.3.1 Fixed Point Theory
6.4.3.2 Spectral Radius
6.4.4 Rate of Convergence
6.4.5 Additional Remarks
6.4.6 Simulation Results
6.5 DPA Generalization to Multi-Source Multi-Destination Cases
6.6 Conclusions
7 Conclusions and Future Work
7.1 Conclusions
7.2 Future Work and Hints for Future Research
A Complements on Chapter 4: Outage Probability
A.1 Preliminary on Outage Probability
A.1.1 Probability Distributions
A.1.2 Sum of Random Variables
A.1.3 Outage Probability
A.2 Interference Classication Based Outage Probability
A.2.1 Noisy Strategy
A.2.2 Joint Decoding Strategy
A.2.3 SIC-Based Strategy
A.2.4 Time-Sharing Strategy
A.2.5 All-in-One Strategy
Bibliography
