Table of contents for Advanced wireless communications : 4G cognitive broadband technologies / Savo G. Glisic.

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1 Fundamentals 
1.1. 4G and the book layout
1.2. General structure of 4G signals
1.2.1 Advanced Time Division Multiple Access-ATDMA
1.2.2 Code Division Multiple Access ? CDMA
1.2.3 Orthogonal Frequency Division Multiplexing ? OFDM
1.2.4 Multicarrier CDMA (MC CDMA)
1.2.5 Ultra Wide Band (UWB) signal
2 Adaptive coding 
2.1 Adaptive and reconfigurable block coding
2.2 Adaptive and Reconfigurable Convolutional Codes
2.2.1 Punctured convolutional codes /code reconfigurability
2.2.2 Maximum likelihood decoding / Viterbi algorithm
2.2.3 Systematic recursive convolutional code
2.3 Concatenated codes with interleaver
2.3.1 The iterative decoding algorithm
2.4 Adaptive coding practice and prospective
2.5 Distributed Source Coding
2.5.1 Continuous valued source 
2.5.2 Scalar Quantization and Trellis-Based Coset Construction
2.5.3 Trellis-Based Quantization and Memoryless 
 Coset Construction: 
2.5.4 Performance Examples
3 Adaptive and Reconfigurable Modulation
3.1. Coded modulation
3.1.1 Trellis Coded Modulation (TCM)
3.1.2 Euclidean distance
3.1.3 Examples of TCM schemes
3.1.4 Two state trellis
3.1.5 Four states trellis
3.1.6 Eight states trellis
3.1.7 QAM 3 bits per symbol
3.1.8 Set partitioning
3.1.9 Representation of TCM
3.1.10 TCM with multidimensional constellation
3.2 Adaptive Coded Modulation for Fading Channels
3.2.1Maintaining fixed distance
4 Space Time Coding
4.1. Diversity gain
4.1.1 Two-branch transmit diversity scheme with one receiver
 The Encoding and Transmission Sequence
 The combining scheme
 ML decision rule
4.1.2 Two transmitters & M receivers
 The Received Signal
The Combiner
 ML decoder
4.1.3 The BER Performance
4.2 Space time coding
4.2.1 The System Model
4.2.2 The Case of Independent Fade Coefficients
4.2.3 Design Criteria for Rayleigh Space-Time Codes
4.2.4 Code Construction
4.2.5 Reconfiguration Efficiency of Space Time Coding 
4.2.6 An r-Space-Time Trellis Code for r > 2
4.2.7 Delay Diversity
4.3 Space-time block codes from orthogonal design
4.3.1 The channel model and the diversity criterion
4.3.2 Real Orthogonal Designs
4.3.3 Space Time Encoder
4.3.4 The diversity order 
4.3.5 The Decoding Algorithm
4.3.6 Linear Processing Orthogonal Designs
4.3.7 Generalized Real Orthogonal Designs
4.3.8 Encoding
4.3.9 The Alamouti Scheme
4.3.10 Complex Orthogonal Designs
4.3.11 Generalized Complex Orthogonal Designs
4.3.12 Special Codes
4.3.13 Performance Results
4.4 Channel Estimation Imperfections
4.5 Quasy Orthogonal Space Time Block Codes
4.5.1 Decoding
4.5.2 Decision metric
4.6 Space time convolutional codes
4.7 Algebraic space time codes
4.7.1 Full spatial diversity
4.7.2 QPSK modulation
4.8 Differential Space -Time Modulation 
4.8.1 Differential decoding
4.9 Multiple Transmit Antenna Differential 
4.9.1 Detection From Generalized Orthogonal Designs
4.9.2 Differential Encoding
4.9.3 Received signal
4.9.4 Orthogonality
4.9.5 Encoding
4.9.6 Differential decoding
4.9.7 Received signal
4.9.8 Demodulation
4.9.9 Multiple receive antennas
4.9.10 The number of transmit antennas less then the number of symbols
4.9.11 Final result 
4.9.12 Real constellation set 
4.10 Layered Space ? Time Coding
4.10.1 Receiver complexity
4.10.2 Group interference suppression
4.10.3 Suppression method
4.10.4 The null space
4.10.5 Receiver
4.10.6 Multilayered Space-Time Coded Modulation
4.10.7 Diversity gain
4.10.8 Adaptive reconfigurable transmit power allocation
 Example 1 
4.10.9 Transmitter
4.10.10 Receiver
 Simulation results
 Example 2
4.10.11 Decoder
4.11 Concatenated Space Time Block Coding
4.11.1 Product sum distance
4.11.2 Error rate bound
4.11.3 The case of low SNR
4.11.4 Code design
4.12 Estimation of MIMO channel
4.12.1 System model
4.12.2 Training
4.12.3 Performance measure
4.12.4 Definitions
4.12.5 Channel estimation error
4.12.6 Error statistic
4.12.7 Performance
 Example M, N=4
4.13 Space-Time Codes for Frequency Selective Channels 
4.13.1 Diversity Gain Properties
4.13.2 Coding Gain Properties
4.13.3 Space-Time Trellis Code Design
4.14 MIMO system optimization
4.14.1 The Channel Model
4.14.2 Gain Optimization by Singular Value Decomposition (SVD)
 Example: (2, 2) system
4.14.3 The General (M, N) Case
4.14.4 Gain Optimization by Iteration for a Reciprocal Channel
4.14.5 Spectral Efficiency of Parallel Channels
4.14.6 Capacity of the (M, N) Array
4.15 MIMO systems with constellation rotation
4.15.1 System model
4.15.2 Performance in a Rayleigh fading channel
14.16 Diagonal Algebraic Space?Time Block Codes
14.16.1 System model
4.16.2 The DAST Coding Algorithm
4.16.3 The DAST decoding algorithm
Appendix 4.1 QR Factorization
Appendix 4.2 Lattice Code Decoder for Space-Time Codes
Appendix 4.3 MIMO channel capacity
5 Multiuser communication 
5.1 Pseudorandom Sequence
5.1.1 Binary Shift-Register Sequences
5.1.2 Properties of Binary maximal-Length Sequence
5.1.3Crosscorrelation Spectra
5.1.4 Maximal Connected Sets of m-sequences
5.1.5 Gold Sequences
5.1.6 Gold-like and Dual-BCH Sequences
5.1.7 Kasami sequences
5.1.8 JPL Sequences
5.1.9 Kroncker Sequences
5.1.10 Walsh functions
	Appendix: Golay codes
5.2 Multiuser CDMA Receivers
5.2.1 Synchronous CDMA channels
5.2.2 The decorrelating detector
5.2.3 The Optimum Linear Multiuser Detector
5.2.4 Multistage Detection in Asynchronous CDMA 
5.2.5 Noncoherent detector
5.2.6 Noncoherent detection in asynchronous multiuser channel 
5.2.7 Multiuser Detection in Frequency 
 Nonselective Rayleigh Fading Channel Multiuser maximum likelihood sequence detection Decorrelating detector
5.2.8 Multiuser Detection in Frequency Selective 
 Rayleigh Fading Channel Multiuser Maximum Likelihood Sequence Detection Viterbi algorithm Coherent Reception with Maximal Ratio Combining
5.3 Minimum Mean-Square Error (MMSE) 
5.3.1 Linear Multiuser Detection
5.3.2 System model in Multipath Fading Channel
5.3.3 MMSE Detector Structures
5.3.4Spatial processing
5.4 Single-user LMMSE Receivers for 
5.4.1 Frequency-Selective Fading Channels
5.4.2 Adaptive precombining LMMSE receivers
5.4.3 The steepest descent algorithm
5.4.4 Blind Adaptive LMMSE-RAKE
5.4.5 Griffiths? algorithm
5.4.6 Constant modulus algorithm
5.4.7 Constrained LMMSE-RAKE, Griffiths? and 
5.4.8 Constant Modulus Algorithms
5.4.9 Blind Least Squares Receivers
5.4.10 Least Square (LS) Receiver
5.4.11 Method Based on the Matrix Inversion Lemma
5.5 Signal Subspace-Based Channel estimation for CDMA systems
5.5.1 Estimating the Signal Subspace
5.5.2 Channel Estimation
5.6 Iterative Receivers for Layered Space Time Coding
5.6.1 LST architectures 
5.6.2 LST receivers 
5.6.3 QR Decomposition /SIC detecor
5.6.4 MMSE/SIC detector
5.6.5 Iterative LST Receivers Iterative Receiver (IR) / PIC Iterative F/B MMSE receiver 
 Appendix : Linear and Matrix Algebra
 Special Matrices
 Matrix Manipulation and Formulas
 Eigendecompostion of Matrices
 Calculation of Eigenvalues and Eigenvectors
6 Channel estimation and equalization
6.1 Equalization in Digital Data Transmission System
6.1.1 Zero-Forcing Equalizers
6.2 LMS Equalizer 
6.2.1 Signal Model
6.2.2 Adaptive weight adjustment
6.2.3 Automatic systems
6.2.4 Iterative algorithm
6.2.5 The LMS algorithm
6.2.6 Decision Feedback Equalizer (DFE)
6.2.7 Blind Equalizers
6.3 Detection for a Statistically Known, Time-varying channel
6.3.1 Signal Model
6.3.2 Channel Model
6.3.3 Statistical Description of the received sequence
6.3.4 The ML Sequence (Block) Estimator for statistically known Channel
6.4 Adaptive MLSE Equalization 
6.4.1 System and Channel Models
6.4.2 Adaptive Channel Estimator and LMS Estimator Model 
6.4.3 The Channel Prediction Algorithm
6.5 Adaptive Channel Identification and Data Demodulation
6.5.1 System Model
6.5.2 Joint Channel and Data Estimation
6.5.3 The Block Sequence Estimation (BSE)
6.5.4 Block Adaptive Channel Estimation
6.5.5 Maximum-likelihood (ML)
6.5.6 Iterative Procedure
6.5.7 Iterative CIR Estimator
6.5.8 Recursive Least Squares (RLS) Channel Estimation
6.5.9 BSE/RLS Algorithm
6.5.10 Data Estimation and Tracking for a Fading Channel
6.5.11 Training
6.5.12 Performance and Computational Complexity
6.5.13 The Statistic Channel Environment
6.5.14 Simulations/BSE/BLMS
6.5.15 The BSE/RLS
6.5.16 The Effect of the Block Length Nb
6.5.17 The convergence properties
6.5.18 The Time-Varying Channel Environment
6.5.19 Performance
6.6. Turbo-Equalization
6.6.1 Signal format
6.6.2 Equivalent discrete time channel model 
6.6.3 Equivalent system state representations
6.6.4 Turbo equalization
6.6.5 Viterbi algorithm 
6.6.6 Iterative implementation of turbo equalization
6.6.7 Performance
6.7 Kalman Filer Based Joint Channel Estimation 
 and Data Detection over Fading Channels
6.7.1 Channel model
6.7.2 The Received Signal
6.7.3 Channel estimation alternatives
6.7.4 Implementing the Estimator
6.7.5 The RLS Algorithm 
6.7.6 The Kalman Filter
6.7.7 Implementation issues
6.7.8 Square Root Filtering
6.8. Equalization using higher order signal statistics
6.8.1 Problem Statement
6.8.2 Signal Model
6.8.3 The Equalizer Coefficients
6.8.4 Stochastic Gradient DFE Adaptive Algorithms
6.8.5 Equivalence of JEM and ISIC
6.8.6 Equivalence of JEM and Decorrelation Criterion
6.8.7 JEM and CMA-DFE
6.8.8 Convergence Analysis
6.8.9 Alternative JEM-DFE Scheme and Bussgang-Type Algorithm
6.8.10 Bussgang-type algorithm [3, Ch. 2]
6.8.11 Convergence of JEM Algorithms
6.8.12 Kurtosis-Based Algorithm
6.8.13 Modified Signal Model
6.8.14 Contrast Function 
6.8.15 Performance example 
7 Orthogonal Frequency Division Multiplexing and MC CDMA
7.1 Timing and Frequency Offset in OFDM
7.1.1 Robust Frequency and Timing Synchronisation for OFDM
7.1.2 Symbol Timing Estimation Algorithm
7.2 Fading Channel Estimation for OFDM Systems 
7.2.1 Diversity receiver 
7.2.2 MMSE Channel Estimation
7.2.3 FIR Channel Estimator
7.2.4 System performance
7.2.5 Reference Generation
7.3 64-DAPSK and 64-QAM Modulated OFDM Signals
7.4 Space Time Coding with OFDM Signals
7.5 Layered Space time coding for MIMO-OFDM
7.5.1 System model (2 times 2 transmit antennas)
7.5.2 Interference cancellation
7.5.3 Four transmit antennas
7.6 Space Time Coded TDMA/OFDM Reconfiguration Efficiency 
7.6.1 Frequency selective channel model
7.6.1 Front end prefilter
7.6.1 Time invariant channel
7.6.1 Optimization problem
7.6.1 Average channel
7.6.1 Prefiltered M-BCJR equalizer
7.6.1 Prefiltered MLSE/DDFSE equalizer complexity
7.6.1 Delayed decision feedback sequence estimation (DDFBSE)
7.6.1 Equalization scheme for STBC
7.6.1 Encoder 
7.6.1 Receiver 
7.6.1 Processing
7.6.1 Channel estimator
7.6.1 Training sequences
7.6.1 Performance 
7.7 Multicarrier CDMA System 
7.7.1 Data Demodulation
7.7.2 Performance 
7.8 Multicarrier DS-CDMA Broadcast Systems 
7.9 Frame by Frame Adaptive Rate Coded 
Multicarrier DS/CDMA System
7.9.1 Transmitter
7.9.2 Receiver
7.9.3 Rate Adaptation
 Illustration Example
7.10 Intermodulation Interference Suppression 
in Multicarrier CDMA Systems
7.10.1 Transmitter
7.10.2 Nonlinear Power Amplifier Model
7.10.3 MMSE Receiver
7.11 Successive Interference Cancellation 
in Multicarrier DS/CDMA Systems
7.11.1 System and Channel Model
7.11.2 Performance
7.12 MMSE Detection of Multicarrier CDMA
7.12.1 Tracking the Fading Processes
7.13 Multiuser Receiver for Space-Time Coded 
 Multicarrier CDMA Systems
7.13.1 Frequency-Selective Fading Channels
7.13.2 Receiver Signal Model of STBC MC-CDMA Systems
7.13.3 Resolving Ambiguity in Blind Approach
7.13.4 Bayesian Optimal Blind Receiver
7.13.5 Blind Bayesian Monte Carlo Multiuser Receiver
7.13.6 Gibbs Sampler
7.13.7 Prior Distributions
7.13.8 Conditional Posterior Distributions
7.13.9 Gibbs Multiuser Detection
7.13.10 Choosing the Sampling Space of Data
7.13.11 Exploiting the Orthogonality Property 
7.13.12 Blind Turbo Multiuser Receiver
 Performance Example
7.14 Parallel interference cancellation in OFDM systems 
 in Time-Varying Multipath Fading Channels
7.15 Zero forcing OFDM equalizer 
 in Time-Varying Multipath Fading Channels 
17.16. Channel Estimation for OFDM Systems 
 Using Multiple Transmit Antennas
7.17 Turbo Processing for an OFDM-Based MIMO System
7.18 PAPR Reduction of OFDM Signals
8 Ultra Wide Band Radio 
8.1 UWB Multiple Access in Gaussian Channel
8.1.1 The multiple access channel
8.1.2 Receiver
8.2 The UWB Channel
8.2.1 Energy capture
8.2.2 The received signal model
8.2.3 The received signal model
8.2.4 The UWB signal propagation experiment 
8.2.5 UWB propagation experiment 2
8.2.6 Clustering models for the indoor multipath propagation channel
8.2.7 Path Loss Modeling
8.2.8 Measurement procedure [ ]
8.2.9 Pathloss modeling
8.2.10 In home channel
8.3 UWB System with M-ary Modulation
8.3.1 Performance in Gaussian channel
8.3.2 Performance in dense multipath
8.3.3 Receiver and BER performance
8.3.4 Time variations
8.3.5 Performance example
8.4 M-ary PPM UWB multiple access
8.4.1 M-ary PPM signal sets
8.4.2 Performance 
8.5. Coded UWB Schemes
8.5.1 Performance
8.5.2 Uncoded system as a coded system with repetition 
8.6 Multiuser detection in UWB radio
8.7 UWB with space time processing
8.7.1 Signal model
8.7.2 Monopulse tracking system
8.8 Beamforming for UWB Radio
8.8.1 Circular array
9 Linear Precoding for MIMO Channels
9.1 Space-Time Precoders and Equalizers for MIMO Channels
9.1.1 ISI modelling in MIMO Channel 
9.1.2 MIMO system precoding and equalization MIMO Bezout equalizer Bezout MIMO Precoder 
9.1.3 Precoder and equalizer design for STBC systems
9.2 Linear precoding based on convex optimization theory
9.2.1 Generalized MIMO system 
9.2.2 Convex optimization
9.2.3 Precoding for power optimization Conic Optimization Solution
9.2.4 Precoder for SINR optimization
9.2.5 Performance example
9.3. Convex Optimization Theory based Beamforming 
9.3.1 Multicarrier MIMO signal model
9.3.2 Channel Diagonalization Optimum Equalizer Optimum Precoder
9.3.3 Convex Optimization Based Beamforming Minimization of the ARITH-MSE Minimization of the GEOM-MSE: Minimization of Maximization of Mutual Information: Minimization of the MAX-MSE Maximization of the ARITH-SINR Maximization of the GEOM-SINR Maximization of the HARM-SINR Maximization of the MIN-SINR Maximization of the PROD-(1+SINR) Minimization of the ARITH-BER 
9.3.4 Constraints in Multicarrier Systems
9.3.5 Performance Examples
10 Cognitive Radio 
10.1 Energy Efficient Adaptive Radio
10.1.1 Frame Length Adaptation
10.1.2 Frame Length Adaptation in Flat-Fading Channel
10.1.3 The Adaptation Algorithm
10.1.4 Energy-Efficient Adaptive Error Control
10.1.5 Error-Control for Speech transmission
10.1.6 Error Control for Data Transmission
10.1.7 IP Packets
10.1.8 Processing-Gain Adaptation
10.1.9 Receiver Algorithm
10.1.10 Trellis based processing/Adaptive 
10.1.11 Maximum Likelihood Sequence Equalizer
10.1.12 Hidden Markov Channel Model
10.1.13 Link-Layer Performance with Inadequate Equalization
10.1.14 Link-Layer Performance with Adequate Equalization
10.1.15 Implementation
10.1.16 Channel and Frequency Tracking Performance
10.1.17 Per-Survivor Processing (PSP)
10.1.18 System Integration
10.1.19 Adaptive Steps
10.1.20 Self-Describing Packets
10.2 A Software Radio Architecture 
for Linear Multiuser Detection
10.2.1 A Unified Architecture for Linear Multiuser Detection
 and Dynamic Reconfigurability
10.2.2 Linear Multiuser Schemes
10.2.3 ?Modified? Filter hk( t )
10.2.4 Variable QoS
10.2.5 Software Radio Architecture for Linear Multiuser Detection
10.2.6 Logical Partitioning of the Architecture
10.2.7 Testbed example 
10.2.8 Partitioning of the Architecture
10.2.9 The Software Radio Architecture
10.2.10 FPGA
10.2.11 DSP
10.2.12 Experimental Results
10.2.13 The Effects of Quantization
10.2.14 The Effect on the ?Near-Far? Resistance
10.3 Reconfigurable ASIC Architecture 
10.3.1 Motivation and present art
10.3.2 Alternative implementations
10.3.3 Example architecture versus an FPGA. 
10.3.4 DSP against the example architecture. 
10.3.5 Fixed Coefficient Filters
10.3.6 Real FIR/correlator
10.3.7 Real IIR/correlator
10.3.8 Cascading Fixed Coefficient Filters: 
10.3.9 Adaptive Filtering
10.3.10 Direct Digital Frequency Synthesis
10.3.11 Resource utilization 
10.3.12 CORDIC algorithm [13],
10.3.13 Discrete Fourier Transform
10.3.14 Goertzel Algorithm
11. Cooperative Diversity in Cognitive Wireless Networks
11.1 System modelling 
11.1.2 Probability of Outage
11.1.3 Cellular Coverage
11.2 Cooperative Diversity Protocols
11.2.1 System and Channel Models 
11.2.2. Coperative diversity protocols
11.2.3 Outage probabilities
11.2.4 Performance bounds for cooperative diversity
11.3 Distributed Space?Time Coding
11.3.1 System Descripon 
11.3.2 BER analysis in DSTC
11.4 Generalisation of distributed space?time-coding 
based on cooperative diversity
11.4.1 System and channel model
11.4.2. Cooperative diversity based on repetition
11.4.3 Cooperative diversity using space time coding
Appendix 11.1: Asymptotic CDF Approximations
Appendix 11.2 Amplify-and-Forward Mutual Information
Appendix 11.3 Input Distributions for Transmit Diversity Bound
12. Cognitive UWB Communications
12.1 Introduction
12.2 Signal and Interference Models 
12.3 Receiver structure and performance 
12.3.1 Interference Rejection Circuit Model
12.4 Performance examples
13. Positioning in Wireless Networks
13.1 Mobile station location in cellular networks 
13.1.1 Introduction
13.1.2 MS location estimation using AD and RD measurements
13.1.3 The circular, hyperbolic, and mixed multilateration 
13.1.4 WLS Solution of the Location Problem
13.1.5 Accuracy measure
13.1.6 Circular multilateration
13.1.7 Hyperbolic Multilateration
13.1.9 Performance results for three stations
13.1.10 Performance results for N stations
13.2 Relative positioning in wireless sensor networks
13.2.1 Performance bounds
13.2.2 Relative location estimation
13.3 Average performance of circular and hyperbolic geolocation
13.3.1 Signal Models and performance limits
13.3.2 Performance of location techniques
13. 3. 3 Average performance of location techniques
14 Channel Modeling and Measurements for 4G
14.1 Macrocellular environments (1.8GHz)
14.1.1 PDF of Shadow Fading
14.2 Urban Spatial Radio Channels in Macro/Micro Cell (2.154 GHz)
14.2.1 Description of Environment
14.3 MIMO Channels in Micro and Pico 
Cell Environment (1.71/2.05 GHz)
14.3.1 Measurement Setups
14.3.2 Validation of the Stochastic 
 MIMO Channel Model Assumptions
14.3.3 Input Parameters to the Validation of the MIMO Model
14.3.4 The Eigenanalysis Method
14.3.5 Validation Procedure
14.4 Outdoor Mobile Channel (5.3GHz)
14.4.1 Spatial and frequency correlations
14.4.2 Path number distribution
14.4.3 Rotation measurements in an urban environment
14.5 Microcell channel (8.45GHz)
14.5.1 Azimuth Profile
14.5.2 Delay Profile for the Forward Arrival Waves
14.5.3 Short-Term Azimuth Spread (AS) for Forward Arrival Waves
14.6 Wireless MIMO LAN environments (5.2GHz)
14.6.1 Data evaluation
14.6.2 Capacity computation
14.6.3 Measurement environments
14.7 Indoor WLAN Channel (17GHz)
14.8 Indoor WLAN Channel (60GHz)
14.8.1 Definition of the Statistical Parameters
14.9 UWB Channel Model
14.9.1 The Large-Scale Statistics
14.9.2 Correlation of MPCs Among Different Delay Bins
14.9.3 The statistical model
14.9.4 Simulations steps
15 Adaptive 4G Networks
15.1 Adaptive MAC Layer
15.1.1 Signal Variations and Power Control Problem
15.1.2 Spectral Efficiency and Effective Load Factor 
 of Multi-rate DS-CDMA PRN
15.1.3 CLSP/DS-CDMA Packet Access and Traffic Model
15.1.4 Bit-Rate Adaptation
15.1.5 The Correlated-Fading Model and Optimal Packet Size
15.1.6 Performance Performance Characteristics of Non-Adaptive System Performance of Adaptive System
 Performance Examples
15.2 Minimum Energy Pear-to-Pear Mobile Wireless Networks
15.2.1 Network Layer Requirements
15.2.2 The Power Consumption Model
15.2.3 Minimum Power Networks
15.2.4 Distributed Network Routing Protocol
15.2.5 Search for Enclosure (Phase 1)
15.2.6 Cost Distribution (Phase 2) 
15.2.7 Computation of the Relay Region
15.2.8 Stationary Network Simulation
15.2.9 Distributed Mobile Networks
15.2.10 Mobile Network Simulation
15.3 Least Resistance Routing in Wireless Networks
15.3.1 Least-Resistance Routing (LRR)
15.3.2 Multimedia Least-Resistance Routing
15.3.3 Network Performance Examples: LRR versus MLRR
 Example 1: Nine-Node Network
 Example 2: Twelve-Node Network
15.3.4 Sensitivity to the Number of Allowable Word Erasures
15.4 Power Optimal Routing in Wireless Networks
 for Guaranteed TCP Layer QoS
15.4.1 Constant End-to-End Error Rate
15.4.2 Optimization Problem
15.4.3 Error Rate Models
15.4.4 Time-invariant Attenuation
15.4.5 Large and Small-Scale Fading
15.4.6 Properties of Power Optimal Paths
15.4.7 Comparing Power Cost Metrics
16 Cognitive Networks and Game Theory
16.1 Cognitive power control 
16.1.1 Noncooperative power control game
16.1.2 Nash equilibrium
16.1.3 Pareto optimality
16.1.4 Supermodular games and social optimality 
16.2 Power control game with QoS guarantee
16.3 Power control game and multiuser detection
16.4 Power control game in MIMO systems
16.5 Game theory based MAC for ad hoc networks
16.6 Tit-for-Tat (TFT) game theory based packet 
 forwarding strategies in ad hoc networks
16.6.1 Strategy models
16.6.2 Network nodes dependency graph and system metamodel
16.6.3 The payoff of iterative game 
16.7 TFT game theory based modelling of 
node cooperation with energy constraint
16.7.1 Acceptance rate
16.7.2 Pareto optimum 
16.7.3 Prisoner?s dilemma and TFT game
16.8 Packet forwarding model based on dynamic Bayesian games
16.9 Game theoretic models for 
routing in wireless sensor networks
16.9.1 Cognitive wireless sensor network model
16.9.2 Optimal routs computation 
16.10 Profit driven routing in cognitive networks
16.10.1 Algorithmic mechanism design
16.10.2 Profit driven pricing mechanism
16.10.3 Truthful behaviour in cognitive networks
16.10.4 Collusion of nodes in cognitive networks
16.11 Game theoretical model of flexible spectra sharing in 
 cognitive networks with social awareness 
16.12 A game theoretical modelling of slotted ALOHA protocol
16.13 Game theory based modeling of 
 admission in competitive wireless networks
16.13.1 System model
16.13.2 Equilibrium Solutions
15.14 Modelling access point pricing as a dynamic game
15.14.1 The system model
16.14.2 Modelling service reselling
16.14.3 File transfer model
16.14.4 Bayesian model for unknown traffic

Library of Congress Subject Headings for this publication:

Wireless communication systems.