Christian Schlegel

Coordinated Multiuser Communications Christian Schlegel 2006 edition

Marc Notes: Includes bibliographical references (p. [249]-259) and indexes. Table of Contents: List of Figures -- List of Tables -- Preface -- 1. Introduction -- 1.1. The Dawn of Digital Communications -- 1.2. Multiple Terminal Networks -- 1.3. Multiple-Access Channel -- 1.4. Degrees of Coordination -- 1.4.1. Transmitter and Receiver Cooperation -- 1.4.2. Synchronization -- 1.4.3. Fixed Allocation Schemes -- 1.5. Network vs. Signal Processing Complexity -- 1.6. Future Directions -- 2. Linear Multiple-Access -- 2.1. Continuous Time Model -- 2.2. Discrete Time Model -- 2.3. Matrix-Algebraic Representation -- 2.4. Symbol Synchronous Model -- 2.5. Principles of Detection -- 2.5.1. Sufficient Statistics and Matched Filters -- 2.5.2. The Correlation Matrix -- 2.5.3. Single-User Matched Filter Detector -- 2.5.4. Optimal Detection -- 2.5.5. Individually Optimal Detection -- 2.6. Access Strategies -- 2.6.1. Time and Frequency Division Multiple-Access -- 2.6.2. Direct-Sequence Code Division Multiple Access -- 2.6.3. Narrow Band Multiple-Access -- 2.6.4. Multiple Antenna Channels -- 2.6.5. Cellular Networks -- 2.6.6. Satellite Spot-Beams Channels -- 2.7. Sequence Design -- 2.7.1. Orthogonal and Unitary Sequences -- 2.7.2. Hadamard Sequences -- 3. Multiuser Information Theory -- 3.1. Introduction -- 3.2. The Multiple-Access Channel -- 3.2.1. Probabilistic Channel Model -- 3.2.2. The Capacity Region -- 3.3. Binary-Input Channels -- 3.3.1. Binary Adder Channel -- 3.3.2. Binary Multiplier Channel -- 3.4. Gaussian Multiple-Access Channels -- 3.4.1. Scalar Gaussian Multiple-Access Channel -- 3.4.2. Code-Division Multiple-Access -- 3.5. Multiple-Access Codes -- 3.5.1. Block Codes -- 3.5.2. Convolutional and Trellis Codes -- 3.6. Superposition and Layering -- 3.7. Feedback -- 3.8. Asynchronous Channels -- 4. Multiuser Detection -- 4.1. Introduction -- 4.2. Optimal Detection -- 4.2.1. Jointly Optimal Detection -- 4.2.2. Individually Optimal Detection: APP Detection -- 4.2.3. Performance Bounds - The Minimum Distance -- 4.3. Sub-Exponential Complexity Signature Sequences -- 4.4. Signal Layering -- 4.4.1. Correlation Detection - Matched Filtering -- 4.4.2. Decorrelation -- 4.4.3. Error Probabilities and Geometry -- 4.4.4. The Decorrelator with Random Spreading Codes -- 4.4.5. Minimum-Mean Square Error (MMSE) Filter -- 4.4.6. Error Performance of the MMSE -- 4.4.7. The MMSE Receiver with Random Spreading Codes -- 4.4.8. Whitening Filters -- 4.4.9. Whitening Filter for the Asynchronous Channel -- 4.5. Different Received Power Levels -- 4.5.1. The Matched Filter Detector -- 4.5.2. The MMSE Filter Detector -- 5. Implementation of Multiuser Detectors -- 5.1. Iterative Filter Implementation -- 5.1.1. Multistage Receivers -- 5.1.2. Iterative Matrix Solution Methods -- 5.1.3. Jacobi Iteration and Parallel Cancellation Methods -- 5.1.4. Stationary Iterative Methods -- 5.1.5. Successive Relaxation and Serial Cancellation Methods -- 5.1.6. Performance of Iterative Multistage Filters -- 5.2. Approximate Maximum Likelihood -- 5.2.1. Monotonic Metrics via the QR-Decomposition -- 5.2.2. Tree-Search Methods -- 5.2.3. Lattice Methods -- 5.3. Approximate APP Computation -- 5.4. List Sphere Detector -- 5.4.1. Modified Geometry List Sphere Detector -- 5.4.2. Other Approaches -- 6. Joint Multiuser Decoding -- 6.1. Introduction -- 6.2. Single-User Decoding -- 6.2.1. The Projection Receiver (PR) -- 6.2.2. PR Receiver Geometry and Metric Generation -- 6.2.3. Performance of the Projection Receiver -- 6.3. Iterative Decoding -- 6.3.1. Signal Cancellation -- 6.3.2. Convergence - Variance Transfer Analysis -- 6.3.3. Simple FEC Codes - Good Codeword Estimators -- 6.4. Filters in the Loop -- 6.4.1. Per-User MMSE Filters -- 6.4.2. Low-Complexity Iterative Loop Filters -- 6.4.3. Examples and Comparisons -- 6.5. Asymmetric Operating Conditions -- 6.5.1. Unequal Received Power Levels -- 6.5.2. Optimal Power Profiles -- 6.5.3. Unequal Rate Distributions -- 6.5.4. Finite Numbers of Power Groups -- 6.6. Proof of Lemma 6.7 -- A. Estimation and Detection -- A.1. Bayesian Estimation and Detection -- A.2. Sufficiency -- A.3. Linear Cost -- A.4. Quadratic Cost -- A.4.1. Minimum Mean Squared Error -- A.4.2. Cramer-Rao Inequality -- A.4.3. Jointly Gaussian Model -- A.4.4. Linear MMSE Estimation -- A.5. Hamming Cost -- A.5.1. Minimum probability of Error -- A.5.2. Relation to the MMSE Estimator -- A.5.3. Maximum Likelihood Estimation -- References -- Author Index -- Subject Index. Publisher Marketing: Coordinated Multiuser Communications provides for the first time a unified treatment of multiuser detection and multiuser decoding in a single volume. Many communications systems, such as cellular mobile radio and wireless local area networks, are subject to multiple-access interference, caused by a multitude of users sharing a common transmission medium. The performance of receiver systems in such cases can be greatly improved by the application of joint detection and decoding methods. Multiuser detection and decoding not only improve system reliability and capacity, they also simplify the problem of resource allocation. Coordinated Multiuser Communications provides the reader with tools for the design and analysis of joint detection and joint decoding methods. These methods are developed within a unified framework of linear multiple-access channels, which includes code-division multiple-access, multiple antenna channels and orthogonal frequency division multiple access.

Contributor Bio:  Schlegel, Christian Schlegel completed the Dip. El. Ing. ETH degree at the Federal Institute of Technology in Zurich in 1984. After gaining some experience in local industry, he travelled to the United States from Switzerland and enrolled at the University of Notre Dame. He obtained the M. S. and Ph. D. degrees in electrical engineering in 1986 and 1988, repectively. In 1988 he joined the Communications Group at the research center of Asea Brown Boveri, Ltd., in Baden, Switzerland, working mainly on mobile communications research projects. He then joined the Digital Communications Group at the University of South Australia in Adelaide as head of the Mobile Communications Research Centre. In 1994 he joined the Division of Engineering at the University of Texas in San Antonio.