06643nam 2200673 450 991083042630332120240219141822.01-282-27851-797866122785180-470-47381-90-470-47380-010.1002/9780470473818(CKB)1000000000789653(EBL)456100(SSID)ssj0000334349(PQKBManifestationID)11266807(PQKBTitleCode)TC0000334349(PQKBWorkID)10258888(PQKB)11660878(MiAaPQ)EBC456100(CaBNVSL)mat05361033(IDAMS)0b0000648117884d(IEEE)5361033(OCoLC)463436650(CaSebORM)9780471779711(PPN)255933274(EXLCZ)99100000000078965320090408h20152009 uy 0engur|n|---|||||txtccrAdvances in multiuser detection /edited by Michael L. Honig1st editionHoboken, New Jersey :Wiley,c2009.1 online resource (517 p.)Wiley series in telecommunications and signal processing ;99Description based upon print version of record.0-471-77971-7 Includes bibliographical references and index.Preface. -- Contributors. -- 1 Overview of Multiuser Detection (Michael L. Honig). -- 1.1 Introduction. -- 1.2 Matrix Channel Model. -- 1.3 Optimal Multiuser Detection. -- 1.4 Linear Detectors. -- 1.5 Reduced-Rank Estimation. -- 1.6 Decision-Feedback Detection. -- 1.7 Interference Mitigation at the Transmitter. -- 1.8 Overview of Remaining Chapters. -- References. -- 2 Iterative Techniques (Alex Grant and Lars K. Rasmussen). -- 2.1 Introduction. -- 2.2 Iterative Joint Detection for Uncoded Data. -- 2.3 Iterative Joint Decoding for Coded Data. -- 2.4 Concluding Remarks. -- References. -- 3 Blind Multiuser Detection in Fading Channels (Daryl Reynolds, H. Vincent Poor, and Xiaodong Wang). -- 3.1 Introduction. -- 3.2 Signal Models and Blind Multiuser Detectors for Fading Channels. -- 3.3 Performance of Blind Multiuser Detectors. -- 3.4 Bayesian Multiuser Detection for Long-Code CDMA. -- 3.5 Multiuser Detection for Long-Code CDMA in Fast-Fading Channels. -- 3.6 Transmitter-Based Multiuser Precoding for Fading Channels. -- 3.7 Conclusion. -- References. -- 4 Performance with Random Signatures (Matthew J. M. Peacock, Iain B. Collings, and Michael L. Honig). -- 4.1 Random Signatures and Large System Analysis. -- 4.2 System Models. -- 4.3 Large System Limit. -- 4.4 Random Matrix Terminology. -- 4.5 Incremental Matrix Expansion. -- 4.6 Analysis of Downlink Model. -- 4.7 Spectral Efficiency. -- 4.8 Adaptive Linear Receivers. -- 4.9 Other Models and Extensions. -- 4.10 Bibliographical Notes. -- References. -- 5 Generic Multiuser Detection and Statistical Physics <Dongning Guo and Toshiyuki Tanaka). -- 5.1 Introduction. -- 5.2 Generic Multiuser Detection. -- 5.3 Main Results: Single-User Characterization. -- 5.4 The Replica Analysis of Generic Multiuser Detection. -- 5.5 Further Discussion. -- 5.6 Statistical Physics and the Replica Method. -- 5.7 Interference Cancellation. -- 5.8 Concluding Remarks. -- 5.9 Acknowledgments. -- References. -- 6 Joint Detection for Multi-Antenna Channels (Antonia Tulino, Matthew R. McKay, Jeffrey G. Andrews,.Iain B. Collings, and Robert W. Heath, Jr.). -- 6.1 Introduction. -- 6.2 Wireless Channels: The Multi-Antenna Realm. -- 6.3 Definitions and Preliminaries. -- 6.4 Multi-Antenna Capacity: Ergodic Regime. -- 6.5 Multi-Antenna Capacity: Non-Ergodic Regime. -- 6.6 Receiver Architectures and Performance. -- 6.7 Multiuser Multi-Antenna Systems. -- 6.8 Diversity-Multiplexing Tradeoffs and Spatial Adaptation. -- 6.9 Conclusions. -- References. -- 7 Interference Avoidance for CDMA Systems (Dimitrie C. Popescu, Sennur Ulukus, Christopher Rose, and Roy Yates). -- 7.1 Introduction. -- 7.2 Interference Avoidance Basics. -- 7.3 Interference Avoidance over Time-Invariant Channels. -- 7.4 Interference Avoidance in Fading Channels. -- 7.5 Interference Avoidance in Asynchronous Systems. -- 7.6 Feedback Requirements for Interference Avoidance. -- 7.7 Recent Results on Interference Avoidance. -- 7.8 Summary and Conclusions. -- References. -- 8 Capacity-Approaching Multiuser Communications Over Multiple Input/Multiple Output Broadcast Channels (Uri Erez and Stephan ten Brink). -- 8.1 Introduction. -- 8.2 Many-to-One Multiple Access versus One-to-Many Scalar Broadcast Channels. -- 8.3 Alternative Approach: Dirty Paper Coding. -- 8.4 A Simple 2 x 2 Example. -- 8.5 General Gaussian MIMO Broadcast Channels. -- 8.6 Coding with Side Information at the Transmitter. -- 8.7 Summary. -- References. -- Index.A Timely Exploration of Multiuser Detection in Wireless Networks During the past decade, the design and development of current and emerging wireless systems have motivated many important advances in multiuser detection. This book fills an important need by providing a comprehensive overview of crucial recent developments that have occurred in this active research area. Each chapter is contributed by noted experts and is meant to serve as a self-contained treatment of the topic. Coverage includes: . Linear and decision feedback methods. Iterative multiuser detection and decoding. Multiuser detection in the presence of channel impairments. Performance analysis with random signatures and channels. Joint detection methods for MIMO channels. Interference avoidance methods at the transmitter. Transmitter precoding methods for the MIMO downlink This book is an ideal entry point for exploring ongoing research in multiuser detection and for learning about the field's existing unsolved problems and issues. It is a valuable resource for researchers, engineers, and graduate students who are involved in the area of digital communications.Wiley series in telecommunications and signal processing ;99Multiuser detection (Telecommunication)Signal theory (Telecommunication)Multiuser detection (Telecommunication)Signal theory (Telecommunication)621.39Honig Michael26363Honig Michael L26363CaBNVSLCaBNVSLCaBNVSLBOOK9910830426303321Advances in multiuser detection4013873UNINA04679nam 22006975 450 991068255450332120251113200705.0981-19-6553-610.1007/978-981-19-6553-1(CKB)5580000000525715(DE-He213)978-981-19-6553-1(EXLCZ)99558000000052571520230318d2023 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierLearning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines Theory, Algorithms and Applications /edited by Jamal Amani Rad, Kourosh Parand, Snehashish Chakraverty1st ed. 2023.Singapore :Springer Nature Singapore :Imprint: Springer,2023.1 online resource (XIV, 305 p. 83 illus., 58 illus. in color.)Industrial and Applied Mathematics,2364-6845981-19-6552-8 Introduction to SVM -- Basics of SVM Method and Least Squares SVM -- Fractional Chebyshev Kernel Functions: Theory and Application -- Fractional Legendre Kernel Functions: Theory and Application -- Fractional Gegenbauer Kernel Functions: Theory and Application -- Fractional Jacobi Kernel Functions: Theory and Application -- Solving Ordinary Differential Equations by LS-SVM -- Solving Partial Differential Equations by LS-SVM -- Solving Integral Equations by LS-SVR -- Solving Distributed-Order Fractional Equations by LS-SVR -- GPU Acceleration of LS-SVM, Based on Fractional Orthogonal Functions -- Classification Using Orthogonal Kernel Functions: Tutorial on ORSVM Package.This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions—Chebyshev, Legendre, Gegenbauer, and Jacobi—are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations. On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.Industrial and Applied Mathematics,2364-6845Algebraic fieldsPolynomialsMathematical optimizationQuantitative researchMachine learningPattern recognition systemsPython (Computer program language)Field Theory and PolynomialsOptimizationData Analysis and Big DataMachine LearningAutomated Pattern RecognitionPythonAlgebraic fields.Polynomials.Mathematical optimization.Quantitative research.Machine learning.Pattern recognition systems.Python (Computer program language).Field Theory and Polynomials.Optimization.Data Analysis and Big Data.Machine Learning.Automated Pattern Recognition.Python.512.3Rad Jamal Amaniedthttp://id.loc.gov/vocabulary/relators/edtParand Kouroshedthttp://id.loc.gov/vocabulary/relators/edtChakraverty Snehashishedthttp://id.loc.gov/vocabulary/relators/edtBOOK9910682554503321Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines3272695UNINA