QoS-Based Resource Allocation and Transceiver Optimization derives a comprehensive theoretical framework for SIR balancing, with and without noise. The theory considers the possible use of receive strategies (e.g. interference filtering or channel assignment), which can be included in the model in an abstract way. Power allocation and receiver design are mutually interdependent, thus joint optimization strategies are derived. QoS-Based Resource Allocation and Transceiver Optimization provides a better understanding of interference balancing and the characterization of the QoS feasible region. It also provides a generic algorithmic framework, which may serve as a basis for the development of new resource allocation algorithms. QoS-Based Resource Allocation and Transceiver Optimization is an invaluable resource for every engineer and researcher working on multiuser interference problems in wireless communications.
Author(s): Martin Schubert, Holger Boche
Year: 2006
Language: English
Pages: 164
193301931X......Page 1
Introduction......Page 14
QoS-based power and resource allocation......Page 16
Related results in wireless communications......Page 19
Outline......Page 22
Axiomatic SIR-Balancing Theory......Page 26
Axiom-based interference model......Page 27
Existence of a min-max optimal power allocation......Page 34
Achievable balanced SIR margin......Page 42
Generalized achievability of SIR targets......Page 45
Special monotonicity properties......Page 47
Comparison of min-max and max-min optimization......Page 49
Summary......Page 50
Min-max balancing and Perron root minimization......Page 52
Characterization of boundary points......Page 62
Achievability under an adaptive receive strategy......Page 69
Uniqueness of the power allocation......Page 76
Irreducible coupling matrices......Page 86
Min-max and max-min balancing......Page 93
Duality......Page 97
Summary......Page 99
Axiomatic interference model......Page 102
Feasibility......Page 104
Sum power minimization and xed-point iteration......Page 107
Relation with SINR balancing......Page 109
Summary......Page 112
Matrix-based interference function......Page 114
Sum-power minimization......Page 115
Matrix-based iteration......Page 117
Convergence and comparison with the xed-point iteration......Page 119
Relationship with spectral radius optimization......Page 123
Application example: Beamforming......Page 129
Summary......Page 134
Geometrical Properties for Log-Convex Interference Functions......Page 136
Log-convexity of linear interference functions......Page 137
Worst-case interference functions......Page 139
Convexity of the QoS feasible region......Page 140
Resource allocation by weighted QoS optimization......Page 145
Summary......Page 146
Acknowledgements......Page 148
A.1 Some de nitions and results......Page 150
A.2 Proof of Theorem 2.9......Page 151
A.3 Proof of Theorem 2.22......Page 152
A.4 Proof of Theorem 3.2......Page 153
A.5 Proof of Theorem 4.3......Page 154
References......Page 156
