Pollin / Timmers Software Defined Radios

From Smart(er) to Cognitive
1. Auflage 2011
ISBN: 978-94-007-1278-2
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 140 Seiten

Many and ever more mobile users wish to enjoy a variety of multimedia services, in very diverse geographical environments. The growing number of communication options within and across wireless standards is accommodating the growing volume and heterogeneity in wireless wishes. On the other hand, advancement in radio technologies opening much more flexibility, a.o. through Software Defined Radios, opens up the possibility to realize mobile devices featuring multi-mode options at low cost and interesting form factors.

It is crucial to manage the new degrees of freedom opened up in radios and standards in a smart way, such that the required service is offered at satisfactory quality as efficiently as possible. Efficiency in energy consumption is clearly primordial for battery powered mobile terminals specifically, and in the context of growing ecological concerns in a broader context. Moreover, efficient usage of the spectrum is a growing prerequisite for wireless systems, and coexistence of different standards puts overall throughput at risk.

The management of flexibility risks bringing about intolerable complexity and hamper the desired agility. A systematic approach, consisting of anticipative preparing for smooth operation, allows mastering this challenge. Case studies show that already today, this approach enables smart operation of radios realizing impressive efficiency gains without hampering Quality-of-Service. In the future wireless communication scenes will be able to profit form the opening of the spectrum. Even smarter and cognitive behavior will become possible and essential.

Autoren/Hrsg.

Pollin, Sofie

Timmers, Michael

Inhaltsverzeichnis

1;Preface;5
2;Contents;6
3;List of Acronyms;9
4;List of Figures;10
5;List of Tables;15
6;Chapter 1: Serving Many Mobile Users in Various Scenarios: Radios to Go Smart(er) and Cognitive;16
6.1;1.1 Towards Cognitive Radio;16
6.2;1.2 Increasing the Hardware Flexibility;16
6.2.1;1.2.1 Wireless Landscape Giving Challenges and Opportunities;17
6.2.1.1;1.2.1.1 Heterogeneity Desires Flexibility;17
6.2.1.2;1.2.1.2 Enabling Seamless Connectivity;18
6.2.1.3;1.2.1.3 Scaling Technology Imposes Reconfigurability;18
6.2.2;1.2.2 The Software-Defined Radio Solution;19
6.3;1.3 Increasing the Policy Flexibility;19
6.3.1;1.3.1 Spectrum: A Scarce Resource;20
6.3.2;1.3.2 The Opportunistic Spectrum Access Solution;20
6.4;1.4 Cognitive Radio: Exploiting Flexibility with Intelligent Control;22
6.5;1.5 The Need for a New Approach;24
6.6;1.6 Radios to Go Smarter and Cognitive;24
7;Chapter 2: Emerging Standards for Smart Radios: Enabling Tomorrow's Operation;26
7.1;2.1 Standards in Evolution;26
7.2;2.2 Hardware Flexibility;27
7.2.1;2.2.1 IEEE 802.11: A Flexible Radio Becomes Smarter;28
7.2.1.1;2.2.1.1 The IEEE 802.11a Physical Layer;28
7.2.1.2;2.2.1.2 The IEEE 802.11n Physical Layer;29
7.2.1.3;2.2.1.3 The IEEE 802.11ac Physical Layer;30
7.2.1.4;2.2.1.4 Multiple Access Through Collision Avoidance with Carrier Sensing;30
7.2.2;2.2.2 3GPP-LTE Evolutions;33
7.2.2.1;2.2.2.1 The 3GPP-LTE Air Interface;33
7.2.2.2;2.2.2.2 Multiple Access in 3GPP-LTE;34
7.2.2.3;2.2.2.3 LTE-Advanced;38
7.3;2.3 Spectrum Access Flexibility;38
7.3.1;2.3.1 The ISM Band: Coexistence in Unlicensed Bands;39
7.3.1.1;2.3.1.1 IEEE 802.11h for Spectrum and Transmit Power Management Extensions;40
7.3.1.2;2.3.1.2 IEEE 802.19 Coexistence Technical Advisory Group;41
7.3.2;2.3.2 The TV White Spaces: Spectrum Sharing in Licensed Bands;41
7.3.2.1;2.3.2.1 IEEE 802.22 Wireless Rural Access Networks;43
7.3.2.2;2.3.2.2 System Overview;44
7.3.2.3;2.3.2.3 Spectrum Sensing;44
7.4;2.4 Operation Across Technologies: Cognitive Radio;46
7.4.1;2.4.1 Mobile Independent Handover: IEEE 802.21;46
7.4.2;2.4.2 Dynamic Spectrum Access Networks: IEEE DYSPAN;47
7.4.2.1;2.4.2.1 IEEE 1900.4;47
7.4.2.2;2.4.2.2 1900.6;49
7.4.3;2.4.3 Reconfigurable Radio Systems: ETSI RSS;49
8;Chapter 3: Cognitive Radio Design and Operation: Mastering the Complexity in a Systematic Way;51
8.1;3.1 The Need for a Strategy;51
8.2;3.2 The Design Landscape Is No Longer Flat;52
8.3;3.3 Design Challenges and Opportunities;53
8.3.1;3.3.1 Design Time Complexity;53
8.3.2;3.3.2 The Mountains We Have to Climb;54
8.3.2.1;3.3.2.1 Channel Dynamics;54
8.3.2.2;3.3.2.2 Application Dynamics;55
8.3.2.3;3.3.2.3 Network Dynamics;55
8.3.3;3.3.3 The Sharing Challenge;56
8.3.3.1;3.3.3.1 The Spectrum Policy;56
8.3.3.2;3.3.3.2 Multi-user Interaction;56
8.3.3.3;3.3.3.3 Fairness;57
8.3.4;3.3.4 Run-Time Complexity;57
8.4;3.4 Proposed Control Framework;58
8.4.1;3.4.1 General Design Concepts;58
8.4.1.1;3.4.1.1 Control Dimensions;58
8.4.1.2;3.4.1.2 Environment Awareness;59
8.4.1.3;3.4.1.3 Efficient and Effective Calibration at Run-Time;60
8.4.2;3.4.2 Design-Time Flow;60
8.4.2.1;3.4.2.1 Design-Time Modeling172;60
8.4.2.2;3.4.2.2 Identify Control Dimensions173;62
8.4.2.3;3.4.2.3 Identify Dynamics174;62
8.4.2.4;3.4.2.4 Mapping to Objective Space175;63
8.4.2.5;3.4.2.5 Cluster and Monitor System Scenarios176177;63
8.4.2.6;3.4.2.6 Determine DT Procedure178;64
8.4.3;3.4.3 Run-Time Operation;64
8.4.3.1;3.4.3.1 Observe the RT Situation172;65
8.4.3.2;3.4.3.2 Map RT Situation to System Scenario173;65
8.4.3.3;3.4.3.3 Execute RT Procedure174;65
8.4.3.4;3.4.3.4 The RT Procedure175;66
8.5;3.5 Conclusions;67
9;Chapter 4: Distributed Monitoring for Opportunistic Radios;68
9.1;4.1 To Not Interfere;68
9.1.1;4.1.1 Problem Context;68
9.1.2;4.1.2 Smart Aspect;69
9.1.3;4.1.3 Outdoor 802.11 Measurements;70
9.1.3.1;4.1.3.1 Measurement Setup;70
9.1.3.2;4.1.3.2 Observations from Measurements;71
9.1.3.2.1;No Clear Trend:;71
9.1.3.2.2;Anisotropic Due to Shadowing:;71
9.1.3.2.3;Noisy Measurements Due to Fast Fading:;71
9.2;4.2 The Sensing Problem;72
9.3;4.3 Distributed Distance-to-Contour Estimation;72
9.3.1;4.3.1 Algorithm Overview and Design Decisions;72
9.3.1.1;4.3.1.1 Local Channel Estimation;73
9.3.1.2;4.3.1.2 Distance-to-Contour Flooding;73
9.3.1.3;4.3.1.3 Iterative Power Control;74
9.3.2;4.3.2 Local Channel Estimation;74
9.3.3;4.3.3 Distance-to-Contour Flooding;76
9.3.3.1;4.3.3.1 Centralized Distance-to-Contour Computation;76
9.3.3.2;4.3.3.2 Distributed Distance-to-Contour Flooding;77
9.3.4;4.3.4 Iterative Power Control;79
9.3.4.1;4.3.4.1 The Increasing Power Scenario;79
9.3.4.2;4.3.4.2 The Decreasing Power Scenario;80
9.3.5;4.3.5 Results;80
9.3.5.1;4.3.5.1 Simulation Model;80
9.3.5.2;4.3.5.2 Results and Discussion;82
9.4;4.4 Conclusions;83
10;Chapter 5: Coexistence: The Whole Is Greater than the Sum of Its Parts;85
10.1;5.1 Introduction;85
10.2;5.2 Modeling Coexistence;86
10.2.1;5.2.1 IEEE 802.15.4 Network Model;86
10.2.2;5.2.2 IEEE 802.11 Interference Model;87
10.2.3;5.2.3 Performance and Energy Measures;88
10.3;5.3 Basic Solution: Random Frequency Selection;89
10.4;5.4 The Problem from a Different Angle;89
10.5;5.5 Scan-Based Approaches;90
10.6;5.6 Distributed Learning and Exploration;91
10.6.1;5.6.1 General Framework;91
10.6.2;5.6.2 Learning Engine;92
10.6.3;5.6.3 Exploration Algorithms;92
10.6.3.1;5.6.3.1 Reward-Based (RB) Exploration;93
10.6.3.2;5.6.3.2 Finding a Cooling Scheme;93
10.6.3.3;5.6.3.3 Adaptive Simulated Annealing;94
10.7;5.7 Simulation Results;95
10.8;5.8 Conclusions;96
11;Chapter 6: Anticipative Energy and QoS Management: Systematically Improving the User Experience;98
11.1;6.1 Energy Efficiency for Smart Radios;98
11.1.1;6.1.1 Minimum Energy at Sufficient QoS;98
11.1.2;6.1.2 Smart Aspects and Energy Efficiency;99
11.2;6.2 Anticipation Through Design Time Modeling;100
11.2.1;6.2.1 Flexibility for Energy and QoS;101
11.2.2;6.2.2 The Varying Context;103
11.2.3;6.2.3 Objectives for Efficient Energy and QoS Management;105
11.2.4;6.2.4 Anticipating the Performance;106
11.3;6.3 Managing the User Experience;109
11.3.1;6.3.1 Smart Resource Allocation Problem Statement;109
11.3.2;6.3.2 Greedy Resource Allocation;110
11.4;6.4 IEEE 802.11a Design Case;112
11.4.1;6.4.1 Energy-Performance Anticipation;113
11.4.2;6.4.2 Anticipative Control in the 802.11 MAC Protocol;115
11.5;6.5 Adapting to the Dynamic Context;117
11.6;6.6 Conclusions;118
12;Chapter 7: Distributed Optimization of Local Area Networks;120
12.1;7.1 Introduction;120
12.2;7.2 Existing Flexibility and Control Mechanisms;121
12.2.1;7.2.1 Optimization of IEEE 802.11 Networks;121
12.2.1.1;7.2.1.1 Transmission Rate;121
12.2.1.2;7.2.1.2 Carrier Sense Threshold;122
12.2.1.3;7.2.1.3 Transmit Power;122
12.2.1.4;7.2.1.4 Hybrid Control;123
12.2.2;7.2.2 Benchmark Solution: Spatial Backoff;123
12.2.2.1;7.2.2.1 Algorithm Overview;123
12.2.2.2;7.2.2.2 Opportunities;124
12.2.3;7.2.3 Multi-Agent Learning;124
12.3;7.3 Spatial Learning: Distributed Optimization of IEEE 802.11 Networks;125
12.3.1;7.3.1 The General Framework;125
12.3.2;7.3.2 The Control Dimensions;127
12.3.2.1;7.3.2.1 Rate;127
12.3.2.2;7.3.2.2 Transmission Power;127
12.3.2.3;7.3.2.3 Carrier Sense Threshold;127
12.3.3;7.3.3 System Scenarios;128
12.3.4;7.3.4 Design-Time Procedures;129
12.3.5;7.3.5 The Learning Engine;131
12.3.6;7.3.6 Seeding the Learning Engine with the DT Procedures;132
12.3.7;7.3.7 Implementation in the IEEE 802.11 MAC Protocol;133
12.3.7.1;7.3.7.1 The Reward;133
12.3.7.2;7.3.7.2 Theoretical Throughput Estimation;134
12.3.7.3;7.3.7.3 Observation Reuse;134
12.4;7.4 Assessing the Gains;135
12.5;7.5 Conclusions;139
13;Chapter 8: Close;140
13.1;8.1 "Good Enough" Is "Close Enough to Optimal";140
13.2;8.2 Closing Remarks: The End Is Not There nor in Sight;142
13.2.1;8.2.1 Keep Moving with the Target;142
14;References;144