Prentice Hall Communications Engineering and Emerging Technologies Series
by Theodore S. Rappaport - Mobile & Portable Radio Research Group Virginia Tech
Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications
- Copyright
- Prentice Hall Communications Engineering and Emerging Technologies Series
- Preface
- 1. Introduction
- 2. IS-95 PCS & Cellular CDMA
- 2.1. Cellular and PCS Frequency Allocation
- 2.2. How IS-95 CDMA PCS Systems Work
- 2.3. Typical Link Budgets for IS-95 PCS
- 2.4. Reverse Traffic Channel Transmission for IS-95
- 2.4.1. Variable Rate Vocoders
- 2.4.2. Error Control - The Frame Quality Indicator and Convolutional Coding
- 2.4.3. Symbol Repetition and Block Interleaving
- 2.4.4. Walsh Functions and 64-ary Orthogonal Modulation
- 2.4.5. Data Burst Randomization and Gating
- 2.4.6. Long Code Spreading
- 2.4.7. Quadrature (Short Code) Spreading
- 2.4.8. Reverse Access Channels
- 2.4.9. Reverse Access - Interaction Between Signals at the Base Station
- 2.5. IS-95 Forward Channel Signals
- 2.6. IS-95 Evolution and cdma2000
- 2.7. Summary
- 3. Introduction to Smart Antennas: Spatial Processing for Wireless Systems
- 3.1. Key Benefits of Smart Antenna Technology
- 3.2. Introduction to Smart Antenna Technology
- 3.3. The Vector Channel Impulse Response and the Spatial Signature
- 3.4. Spatial Processing Receivers
- 3.5. Fixed Beamforming Networks
- 3.6. Switched Beam Systems
- 3.7. Adaptive Antenna Systems
- 3.8. Wideband Smart Antennas
- 3.9. Spatial Diversity, Diversity Combining, and Sectoring
- 3.10. Digital Radio Receiver Techniques and Software Radios for Smart Antennas
- 3.11. Transmission Beamforming
- 3.12. Array Calibration
- 3.13. Summary
- 4. Smart Antennas Techniques for CDMA
- 5. CDMA System Range and Capacity Improvement Using Spatial Filtering
- 5.1. Range Extension In CDMA
- 5.2. Single Cell Systems with Spatial Filtering at the IS-95 Base Station
- 5.3. Reverse Channel Performance of Multi-cell Systems with Spatial Filtering at the Base Station
- 5.4. Reverse Channel Spatial Filtering at the WLL Subscriber Unit
- 5.5. Range and Capacity Analysis Using Smart Antennas - A Vector-Based Approach
- 5.6. Summary
- 6. Characterization of Spatio-Temporal Radio Channels
- 6.1. Wireless Multipath Channel Models, Environment, and Signal Parameters
- 6.2. Spatio-Temporal Channel Models for Smart Antennas
- 6.2.1. Lee’s Model
- 6.2.2. Stapleton’s Extension of Lee’s Model
- 6.2.3. Discrete Uniform Distribution
- 6.2.4. Geometrically Based Single Bounce Statistical Channel Models
- 6.2.5. 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) Model
- 6.2.8. 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) Model
- 6.2.12. The Bad Urban (BU) Model
- 6.2.13. The Uniform Sectored Distribution Model
- 6.2.14. Modified Saleh-Valenzuela’s Model
- 6.2.15. Extended Tap-Delay-Line Method
- 6.2.16. Elliptical Subregions Model (Lu, Lo, and Litva’s Model)
- 6.2.17. Measurement Based Channel Model
- 6.2.18. Ray Tracing Models
- 6.3. Spatial Channel Measurements
- 6.4. Application of Spatial Channel Models
- 6.5. Summary
- 7. The Geometrically Based Single Bounce Elliptical Model
- 8. Optimal Spatial Filtering and Adaptive Algorithms
- 8.1. Impact of Multipath on Optimal Spatial Filtering
- 8.2. Performance of Underloaded and Overloaded Adaptive Arrays
- 8.3. Adaptive Algorithms
- 8.4. Adaptive Algorithms for CDMA
- 8.5. Multitarget Decision-Directed Algorithm (MT-DD)
- 8.6. Least Squares De-spread Re-spread Multitarget Array (LS-DRMTA)
- 8.7. Least Squares De-spread Re-spread Multitarget Constant Modulus Algorithm
- 8.8. Summary
- 9. Direction-Of-Arrival Estimation Algorithms
- 9.1. Conventional Methods for DOA Estimation
- 9.2. Subspace Methods for DOA Estimation
- 9.3. Maximum Likelihood Techniques
- 9.4. DOA Estimation under Coherent Signal Conditions
- 9.5. The Iterative Least Squares Projection Based CMA
- 9.6. The Integrated Approach to DOA Estimation
- 9.7. Detection of Number of Sources in Eigen Decomposition
- 9.8. Summary
- 10. RF Position Location Systems
- A. Multiple Access Interference and the Gaussian Approximation
- B. Q, erf, & erfc Functions
- C. Mathematical Tables
- D. Abbreviations and Acronyms
- References
- The Authors
Theodore S. Rappaport, Series Editor
LIBERTI & RAPPAPORT Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications
GARG & WILKES Principles and Applications of GSM
RAPPAPORT Wireless Communications: Principles & Practice
RAZAVI RF Microelectronics
STARR, CIOFFI & SILVERMAN Understanding Digital Subscriber Line Technology
CIMINI & LI Orthogonal Frequency Division Multiplexing for Wireless Communication
GARG & HALPERN CDMA Networks and CDMA Technologies
POOR & WANG Wireless Communication Systems: Advanced Techniques for Signal Reception
REED & WOERNER Software Radio: A Modern Approach to Radio Engineering
TRANTER, KOSBAR, RAPPAPORT & SHANMUGAN Simulation of Modern Communications Systems with Wireless Applications
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