book

Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications

Name: Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications
ISBN: 0137192878

by Jr. - Bellcore Joseph C. Liberti, Theodore S. Rappaport - Mobile & Portable Radio Research Group Virginia Tech

April 1999

Intermediate to advanced

528 pages

10h 6m

English

Pearson

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Copyright
Dedication
Prentice Hall Communications Engineering and Emerging Technologies Series
Forthcoming
Preface
1. Introduction
1.1. The Cellular Radio Concept1.2. Evolution of Wireless Communications1.2.1. Key Terms and Concepts in Wireless Communications1.2.2. Digital Cellular and PCS Technologies1.2.3. CDMA Wireless Local Loop1.2.4. MMDS and LMDS1.2.5. Third Generation (3G) Wireless Systems1.3. Spread Spectrum and Code Division Multiple Access1.3.1. Direct-Sequence Spread Spectrum1.3.2. Multiple Access Interference in DS-CDMA Systems1.3.3. Power Control and the Near-Far Problem in CDMA1.3.4. Frequency Hop Spread Spectrum1.4. Antenna Systems1.5. Basic Concepts in Radiowave Propagation1.5.1. Path Loss in Real World Channels1.6. Small Scale Fading1.7. Large Scale Path Loss1.7.1. Log-distance Path Loss Model1.7.2. Log-normal Shadowing1.8. Summary
2. IS-95 PCS & Cellular CDMA
2.1. Cellular and PCS Frequency Allocation2.1.1. PCS Frequency Allocation2.2. How IS-95 CDMA PCS Systems Work2.2.1. Soft and Softer Handoff in IS-95 CDMA Systems2.3. Typical Link Budgets for IS-95 PCS2.3.1. IS-95 Forward Link Budget for 1900 MHz2.3.2. IS-95 Reverse Link Budget at 1900 MHz2.4. Reverse Traffic Channel Transmission for IS-952.4.1. Variable Rate Vocoders2.4.2. Error Control - The Frame Quality Indicator and Convolutional Coding2.4.3. Symbol Repetition and Block Interleaving2.4.4. Walsh Functions and 64-ary Orthogonal Modulation2.4.5. Data Burst Randomization and Gating2.4.6. Long Code Spreading2.4.7. Quadrature (Short Code) Spreading2.4.8. Reverse Access Channels2.4.9. Reverse Access - Interaction Between Signals at the Base Station2.5. IS-95 Forward Channel Signals2.5.1. Forward Channel Transmitter Structures2.5.2. The Pilot Channel2.5.3. The Sync Channel2.5.4. Paging Channels2.5.5. Forward Traffic Channels2.5.6. The Power Control Subchannel2.5.7. Downlink Power Control2.6. IS-95 Evolution and cdma20002.7. Summary
3. Introduction to Smart Antennas: Spatial Processing for Wireless Systems
3.1. Key Benefits of Smart Antenna Technology3.2. Introduction to Smart Antenna Technology3.3. The Vector Channel Impulse Response and the Spatial Signature3.4. Spatial Processing Receivers3.5. Fixed Beamforming Networks3.6. Switched Beam Systems3.7. Adaptive Antenna Systems3.8. Wideband Smart Antennas3.9. Spatial Diversity, Diversity Combining, and Sectoring3.10. Digital Radio Receiver Techniques and Software Radios for Smart Antennas3.11. Transmission Beamforming3.12. Array Calibration3.13. Summary
4. Smart Antennas Techniques for CDMA
4.1. Non-Coherent CDMA Spatial Processors4.2. Coherent CDMA Spatial Processors and the Spatial Processing Rake Receiver4.3. Multi-User Spatial Processing4.4. Dynamic Re-sectoring Using Smart Antennas4.5. Downlink Beamforming for CDMA4.6. Summary
5. CDMA System Range and Capacity Improvement Using Spatial Filtering
5.1. Range Extension In CDMA5.2. Single Cell Systems with Spatial Filtering at the IS-95 Base Station5.3. Reverse Channel Performance of Multi-cell Systems with Spatial Filtering at the Base Station5.4. Reverse Channel Spatial Filtering at the WLL Subscriber Unit5.5. Range and Capacity Analysis Using Smart Antennas - A Vector-Based Approach5.6. Summary
6. Characterization of Spatio-Temporal Radio Channels
6.1. Wireless Multipath Channel Models, Environment, and Signal Parameters6.1.1. The Macrocell Environment6.1.2. The Microcell Environment6.1.3. Angle Spread6.1.4. Measuring Time Variation of the Channel6.2. Spatio-Temporal Channel Models for Smart Antennas6.2.1. Lee’s Model6.2.2. Stapleton’s Extension of Lee’s Model6.2.3. Discrete Uniform Distribution6.2.4. Geometrically Based Single Bounce Statistical Channel Models6.2.5. The Geometrically Based Single Bounce Circular Model (Macrocell Model)6.2.6. The Geometrically Based Single Bounce Elliptical Model (Microcell Model)6.2.7. The Gaussian Wide Sense Stationary Uncorrelated Scattering (GWSSUS) Model6.2.8. Gaussian Angle-Of-Arrival (GAA)6.2.9. Time-Varying Vector Channel Model (Raleigh’s Model)6.2.10. Two GSM Simulation Models (TU and BU)6.2.11. The Typical Urban (TU) Model6.2.12. The Bad Urban (BU) Model6.2.13. The Uniform Sectored Distribution Model6.2.14. Modified Saleh-Valenzuela’s Model6.2.15. Extended Tap-Delay-Line Method6.2.16. Elliptical Subregions Model (Lu, Lo, and Litva’s Model)6.2.17. Measurement Based Channel Model6.2.18. Ray Tracing Models6.3. Spatial Channel Measurements6.4. Application of Spatial Channel Models6.5. Summary
7. The Geometrically Based Single Bounce Elliptical Model
7.1. Simulation of Multipath Component Parameters in GBSBEM7.2. Marginal Distribution of the Direction-Of-Arrival in the GBSBEM Model7.3. Doppler Spectra and the Fading Envelope7.4. Selection of the Maximum Path Delay, τm7.5. Summary

8. Optimal Spatial Filtering and Adaptive Algorithms
8.1. Impact of Multipath on Optimal Spatial Filtering8.1.1. Flat Fading Channels8.1.2. Frequency Selective/Time Dispersive Channels8.1.3. Array Performance in Multipath8.2. Performance of Underloaded and Overloaded Adaptive Arrays8.3. Adaptive Algorithms8.3.1. Blind Adaptive Algorithms8.3.2. The Least Squares Constant Modulus Algorithm8.4. Adaptive Algorithms for CDMA8.4.1. Multitarget Least Squares Constant Modulus Algorithm8.4.2. Gram-Schmidt Orthogonalization8.4.3. Phase Ambiguity8.4.4. Sorting Procedure8.5. Multitarget Decision-Directed Algorithm (MT-DD)8.6. Least Squares De-spread Re-spread Multitarget Array (LS-DRMTA)8.6.1. Derivation of LS-DRMTA8.6.2. Advantages of LS-DRMTA8.7. Least Squares De-spread Re-spread Multitarget Constant Modulus Algorithm8.7.1. Derivation of LS-DRMTCMA8.7.2. Advantages of LS-DRMTCMA8.8. Summary
9. Direction-Of-Arrival Estimation Algorithms
9.1. Conventional Methods for DOA Estimation9.1.1. Delay-and-Sum Method9.1.2. Capon’s Minimum Variance Method9.2. Subspace Methods for DOA Estimation9.2.1. The MUSIC Algorithm9.2.2. Improvements to the MUSIC Algorithm9.2.3. Root-MUSIC Algorithm9.2.4. Cyclic MUSIC Algorithm9.2.5. The ESPRIT Algorithm9.3. Maximum Likelihood Techniques9.4. DOA Estimation under Coherent Signal Conditions9.4.1. Spatial Smoothing Techniques9.4.2. Multidimensional MUSIC9.5. The Iterative Least Squares Projection Based CMA9.6. The Integrated Approach to DOA Estimation9.7. Detection of Number of Sources in Eigen Decomposition9.7.1. The SH, MDL and AIC Criteria9.7.2. Order Estimation Using Transformed Gerschgorin Radii9.8. Summary
10. RF Position Location Systems
10.1. Direction Finding PL Systems10.2. True Ranging PL Systems10.3. Elliptical PL Systems10.4. Hyperbolic PL Systems10.5. Hyperbolic versus DF PL Systems10.6. TDOA Estimation Techniques10.6.1. General Model for TDOA Estimation10.6.2. Generalized Cross-Correlation Methods10.6.3. Hyperbolic Position Location Estimation Technologies10.6.4. Methods for Hyperbolic PL Estimation10.7. Measures of Position Location Accuracy10.7.1. MSE and the Cramér-Rao Lower Bound10.7.2. Circular Error Probability10.7.3. Geometric Dilution of Precision10.8. Summary
A. Multiple Access Interference and the Gaussian Approximation
A.1. The Gaussian Approximation
B. Q, erf, & erfc Functions
B.1. The Q-FunctionB.2. The erf and erfc functions
C. Mathematical Tables
D. Abbreviations and Acronyms
ABCDEFGHIJLMNOPQRSTUVWXZ
References
The Authors

Content preview from Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications

Chapter 7. The Geometrically Based Single Bounce Elliptical Model

In the previous chapter, an overview of spatial channel models, metrics, and measurements was presented. Here we describe one particular model, the Geometrically Based Single Bounce Elliptical Model (GBSBEM) [Lib95][Lib96a]. The complete derivation of the model is provided. The model is used to simulate power-delay-angle profiles (pda-grams), power delay profiles, joint time-angle statistics, marginal characteristics of the Direction-Of-Arrival, and narrowband fading envelopes. The GBSBEM model is appropriate for low-tier systems, including microcell and picocell systems, where base station antennas are surrounded by clutter, and scatterers are distributed between and around both ...

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Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications

by Jr. - Bellcore Joseph C. Liberti, Theodore S. Rappaport - Mobile & Portable Radio Research Group Virginia Tech

Chapter 7. The Geometrically Based Single Bounce Elliptical Model

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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