Оглавление
Lin Bai. Spatial Multidimensional Cooperative Transmission Theories And Key Technologies
SPATIAL MULTIDIMENSIONAL. COOPERATIVE TRANSMISSION. Theories and Key Technologies
Preface
About the Authors
Acknowledgments
Common Symbol Table
Contents
Chapter 1. Introduction
1.1Overview of Ground-based Wireless Communication System
1.1.1The first generation of mobile communication system
1.1.2The second generation of mobile communication system
1.1.3The third generation of mobile communication system
1.1.4The fourth generation of mobile communication system
1.1.5The fifth generation of mobile communication system
1.2The Overview of Air-Based Cooperative Transmission System
1.3The Overview of Space-Based Cooperative Transmission System
1.3.1The current situation and development trend of space-based cooperative transmission system
1.3.2The basic principle of space-based cooperative transmission system
1.4Summary
References
Chapter 2. The Overview of Multi-Antenna Signal and System
2.1Spatial Signal Combination and Detection Basis
2.1.1Spatial signal combination
2.1.1.1The combining method of known channels
2.1.1.2The combining method of unknown channels
2.1.2Received signal detection
2.1.2.1Binary waveform signal detectiona
2.1.2.2M-ary signal detection
2.1.2.3Signal detection in vector space
2.2Array Antenna Pattern Synthesis Technology
2.2.1Array antenna arrangement
2.2.2Array antenna freedom
2.2.3Array antenna pattern synthesis
2.2.3.1Dolph–Chebyshev pattern synthesis method
2.2.3.2Taylor single-parameter pattern synthesis method
2.3Overview of the MIMO System
2.3.1Diversity technique
2.3.2SIMO system
2.3.2.1Reception diversity through selective combining
2.3.2.2Reception diversity based on gain combining
2.3.2.3Reception diversity through hybrid selection combining or gain combining
2.3.3MISO system
2.3.3.1Transmit diversity formed by matched beamforming
2.3.3.2Space–time coded transmit diversity
2.3.3.3Indirect transmit diversity
2.3.4MIMO system
2.3.4.1MIMO system with complete transmit channel information
2.3.4.2MIMO system without transmit channel information
2.3.4.3MIMO system with partial transmit channel information
2.4MIMO Traditional Detection Technology
2.4.1System model
2.4.2Uncoded MIMO signal detectionb. 2.4.2.1Maximum likelihood MIMO signal detection
2.4.2.2Linear MIMO signal detection
2.4.2.3Successive interference cancellation (SIC) detection
2.4.3Simulation results
2.5Summary
References
Chapter 3. Adaptive Antenna Array Theory and Technology
3.1The Basic Principle of Adaptive Antenna Array
3.2Optimal Filtering Criteria
3.2.1Minimum mean square error criterion
3.2.2Maximum signal-to-interference-and-noise ratio criterion
3.2.3Maximum likelihood criterion
3.2.4Minimum variance criterion
3.3Adaptive Beamforming Algorithm
3.3.1Least mean square algorithm
3.3.1.1Algorithm stability
3.3.1.2Algorithm convergence speed
3.3.1.3Algorithm misadjustment
3.3.2Sample matrix inversion
3.3.3Recursive least squares
3.3.4Conjugate gradient algorithm
3.3.5Constant modulus algorithm
3.4Direction of Arrival Estimation
3.4.1Traditional spectral estimation method
3.4.2Maximum entropy spectrum estimation
3.4.3Multiple signal classification algorithm
3.4.4Estimation of signal parameters via rotational invariance techniques
3.4.5Maximum likelihood algorithm
3.4.6Subspace fitting algorithm
3.4.6.1Signal subspace fitting
3.4.6.2Noise subspace fitting
3.5Adaptive Antenna Array Calibration
3.5.1Radio feed reference signal method
3.5.2Signal injection method
3.5.3Blind signal calibration method
3.6Adaptive Antenna System Hardware Architecture
3.6.1Radio frequency front-end module
3.6.2Data signal processing module
3.6.3Parallel digital beamforming
3.7Summary
References
Chapter 4. MIMO Multi-Antenna Theory and Technology
4.1MIMO Channel Model
4.2MIMO Channel Capacity
4.2.1Deterministic channel capacity
4.2.2Random MIMO channel capacity
4.2.3Comparison of MIMO channel capacity for average power allocation
4.2.3.1SISO channel capacity
4.2.3.2MISO channel capacity
4.2.3.3SIMO channel capacity
4.2.3.4MIMO channel capacity
4.3MIMO Space–Time Coding Technology
4.3.1Space–time coding and coding guidelines
4.3.2Space–time trellis code
4.3.3Space–time block code
4.3.4Layered space–time code
4.3.5Other space–time coding
4.4MIMO Beamforming Technology
4.4.1Single-user beamforming
4.4.2Multi-user beamforming
4.4.2.1Multi-user MIMO system model
4.4.2.2Dirty paper coding
4.4.2.3Zero-forcing beamforming
4.4.2.4Block diagonal beamforming
4.4.2.5Opportunistic beamforming
4.5MIMO Multi-antenna Technology
4.5.1Mutual coupling of multiple antenna units
4.5.2Spatial correlation coefficient
4.5.3Spatial correlation and MIMO channel
4.5.4MIMO multi-antenna decoupling
4.5.4.1Decoupling technology with parasitic radiating elements
4.5.4.2Separate ground-plane structure decoupling
4.5.4.3Special ground-plane structure decoupling technology
4.5.4.4Filter mechanism decoupling
4.5.4.5Additional decoupling network
4.5.5MIMO multi-antenna selection
4.5.5.1Multimode antenna
4.5.5.2Switched parasitic antenna
4.5.5.3Printed antenna array such as the fractal antenna and the planar inverted-F antenna
4.5.5.4High dielectric antenna array
4.5.5.5Photonic band gap/electromagnetic band gap substrate antenna array
4.5.5.6Compact cube layout antenna array
4.5.5.7Three-dimensional orthogonal layout monopole antenna array
4.6Massive MIMO Technology
4.6.1Massive MIMO system application prospects
4.6.2Channel Hardening under large sizes
4.6.3Technical challenges faced by large-scale MIMO
4.6.3.1Independent spatial dimension availability
4.6.3.2Multi-antenna and RF chain layout
4.6.3.3Low-complexity large-scale MIMO signal processing
4.6.3.4Multi-cellular processing
4.7Summary
References
Chapter 5. Spatial Multidimensional Signal Reception and Iterative Processing
5.1MIMO Detection Technology Based on Lattice Theorya
5.1.1Lattice mathematical basis
5.1.2MIMO detection based on lattice reduction
5.1.2.1LLL algorithm
5.1.2.2Linear detection based on LR
5.1.2.3Performance evaluation
5.1.3Simulation results
5.2Iterative Detection and Decoding Basic Principle and Optimal MAP Detectionb
5.2.1BICM-ID system
5.2.2MIMO iterative receiver-optimal MAP detection
5.3Detection and Decoding Technology Based on Random Sampling
5.3.1System model
5.3.2LR-based sampling list generation method
5.3.2.1Gaussian estimation in the LR domain
5.3.2.2LR-based random IDD
5.3.2.3Complex-valued LR-based list generation
5.3.3Complexity analysis
5.3.3.1Complexity analysis and reduction
5.3.3.2Complexity comparison
5.3.4Simulation results
5.4Bit Filtering-Based Detection and Decoding Technology
5.4.1LR-based IDD and bit-level combination and list generation. 5.4.1.1Design of bit-level MMSE filter based on LR
5.4.1.2Generating the integer perturbation list
5.4.2Complexity analysis
5.4.3Simulation results
5.4.3.1Comparison of LR-IDD-1 and LR-IDD-2
5.4.3.2Complexity comparison
5.4.3.3Convergence analysis
5.4.3.4BER performance
5.5Summary
References
Chapter 6. Ground-Based Cooperative Transmission System
6.1Ground-Based Transmission System Overview
6.1.1Development of ground-based wireless communication systems. 6.1.1.1Wireless communication system in the early stages
6.1.1.2The third-generation wireless communication system
6.1.1.3The fourth-generation wireless communication system
6.1.1.4The future wireless communication system development trend
6.1.2Characteristics of ground-based wireless communication systems
6.2Multidimensional Joint Resource Management of Ground-Based Wireless Communication System
6.2.1Radio resource management model based on a two-layer cognitive loop
6.2.1.1Demand analysis
6.2.1.2Two-layer cognitive loop model
6.2.2Intelligent radio resource management model
6.2.2.1Advanced radio resource management
6.2.2.2Cognitive engine
6.2.2.3Local radio resource management
6.2.2.4Terminal decision management
6.2.3Service-oriented radio resource management implementation architecture
6.2.4MIMO–OFDM system radio resource scheduling
6.2.4.1Spatial multiplexing-based OFDM technology
6.2.4.2OFDM technology based on space–time coding/space–frequency coding/space–time–frequency coding
6.2.4.3Beamforming-based OFDM technology
6.2.4.4OFDM technology based on antenna selection
6.2.4.5Multi-user MIMO–OFDM technology
6.3Multi-User Cooperative Transmission Method
6.3.1Orthogonal beamforming technology. 6.3.1.1System model
6.3.1.2The user selection of group A
6.3.1.3Orthogonal beamforming
6.3.2Beamforming technology for multi-user relay systems
6.3.3Multi-user selection strategya
6.3.3.1System model
6.3.3.2LR base iterative method
6.4Multi-cell Cooperative Transmission and Anti-Interference Method
6.4.1Multi-cell cooperative transmission
6.4.1.1Cooperative schedule/beamforming (CS/CB)
6.4.1.2Joint processing (JP) technology
6.4.2Geometric modeling of multi-cell interference system
6.4.3Multi-cell system anti-interference technology
6.4.3.1Interference randomization
6.4.3.2Interference cancellation
6.4.3.3Interference coordination/avoidance
6.4.4Multi-cell system cooperative interference suppression
6.4.4.1Joint zero-forcing
6.4.4.2Joint diagonalization
6.4.4.3Joint dirty paper coding
6.5The Massive MIMO System
6.5.1Review of the basic concepts of a massive MIMO System
6.5.2Single-user massive MIMO
6.5.3Multi-user massive MIMO. 6.5.3.1Massive MIMO downlink channel
6.5.3.2Massive MIMO upstream channel
6.5.4Multi-cell massive MIMO
6.6Summary
References
Chapter 7. Air-Based Cooperative Transmission System
7.1Overview of Air-Based Transmission Technology
7.1.1Introduction to the high-altitude platform communication system
7.1.2Introduction to Project Loon
7.2Array-Based and Air-Based Transmission System
7.2.1Mathematical model of the antenna beam
7.2.2Efficient algorithm for predicting co-channel interference
7.2.3121 cell structure results
7.2.4Conclusions
7.3Air-Based Beamforming
7.3.1Two-dimensional spatial interpolation beamformer (2D SIB) 7.3.1.1Two-dimensional prototype beamformer
7.3.1.22D SSF and 2D SMF
7.3.2Design example of two-dimensional spatial interpolation filter
7.4High-Altitude Platform Cell Planning
7.4.1Coverage and cell partition of high-altitude platforms. 7.4.1.1Coverage of high-altitude platforms
7.4.1.2Cell partition
7.4.1.3The calculations of wireless connection
7.4.2Conclusions
7.5High-Altitude Platform Transmission Mechanism
7.5.1The introduction of related technologies
7.5.2System and channel model
7.5.3Singular vector-based training method
7.5.4Steering vector-based training method
7.5.5Performance evaluation
7.5.6Conclusions
7.6Summary
References
Chapter 8. Space-Based Cooperative Transmission System
8.1The Overview of Space-Based Transmission Technology
8.2Constellation Cooperative Multi-Beam Transmission Technology
8.3Constellation Cooperative MIMO System Modeling
8.3.1Single-antenna constellation
8.3.2Array antenna constellation
8.4Constellation-Cooperative MIMO System Capacity
8.4.1Capacity derivation
8.4.2Single-antenna constellation capacity
8.4.2.1Establish LOS orthogonal channel
8.4.2.2Optimize antenna layout in combination with geometric position
8.4.2.3Simulation comparison
8.4.3Array antenna constellation capacity
8.4.3.1Establish LOS orthogonal channel
8.4.3.2Optimize the antenna layout in combination with geometric position
8.4.3.3Simulation comparison
8.5Analysis of Factors Affecting Capacity of Constellation Cooperative MIMO System
8.5.1Single-antenna constellation
8.5.1.1Description and classification of factors affecting channel fading
8.5.1.2Possible factors affecting ground terminals
8.5.1.3Channel fading caused by satellite maneuvering
8.5.2Array antenna constellation
8.5.2.1Description and classification of factors affecting channel fading
8.5.2.2Possible factors from ground terminals
8.5.2.3Channel fading caused by satellite maneuvering
8.6Summary
References
Conclusion
Index
