Foundations and trends in communications and information theory,
v. 2, no. 6
1567-2328 ;
Title from PDF title page (NOW, viewed Jun. 3, 2010).
Includes bibliographical references.
Abstract -- 1. Introduction -- 2. Axiomatic SIR-balancing theory -- 3. Matrix-based SIR balancing -- 4. General SINR balancing theory -- 5. Matrix-based SINR balancing and algorithmic solutions -- 6. Geometrical properties for log-convex interference functions -- Acknowledgements -- Appendix -- References.
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The control and reduction of multiuser interference is a fundamental problem in wireless communications. In order to increase the spectral efficiency and to provide individual quality-of-service (QoS), it is required to jointly optimize the power allocation together with possible receive and transmit strategies. This often leads to complex and difficult-to-handle problem formulations. There are many examples in the literature, where the special structure of the problem is exploited in order to solve special cases of this problem (e.g. multiuser beamforming or CDMA). So it is desirable to have a general theory, which can be applied to many practical QoS measures, like rates, delay, BER, etc. These measures can all be related to the signal-to-interference ratio (SIR) or the signal-to-interference-plus-noise ratio (SINR). This leads to the problem of SIR and SINR balancing, which is fundamental for many problems in communication theory. In this text we derive 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. The main purpose of this text is to provide a better understanding of interference balancing and the characterization of the QoS feasible region. We also provide a generic algorithmic framework, which may serve as a basis for the development of new resource allocation algorithms.
01251032
QoS-Based Resource Allocation and Transceiver Optimization.
9781933019314
Foundations and Trends Bundle
Data transmission systems.
Mathematical optimization.
Signal processing.
Signal theory (Telecommunication)
COMPUTERS-- Information Theory.
Data transmission systems.
Mathematical optimization.
Signal processing.
Signal theory (Telecommunication)
TECHNOLOGY & ENGINEERING-- Signals & Signal Processing.