Algorithms for communications systems and their applications

✍ Scribed by Stefano Tomasin; Nevio Benvenuto; Giovanni Cherubini

• Year: 2020
• Tongue: English
• Leaves: 961
• Edition: Second
• Category: Library

✦ Table of Contents

Cover
Title Page
Copyright
Contents
Preface
Acknowledgements
Chapter 1 Elements of signal theory
1.1 Continuous‐time linear systems
1.2 Discrete‐time linear systems
Discrete Fourier transform
The DFT operator
Circular and linear convolution via DFT
Convolution by the overlap-save method
IIR and FIR filters
1.3 Signal bandwidth
The sampling theorem
Heaviside conditions for the absence of signal distortion
1.4 Passband signals and systems
Complex representation
Relation between a signal and its complex representation
Baseband equivalent of a transformation
Envelope and instantaneous phase and
1.5 Second‐order analysis of random processes
1.5.1 Correlation
Properties of the autocorrelation function
1.5.2 Power spectral density
Spectral lines in the PSD
Cross power spectral density
Properties of the PSD
PSD through
1.5.3 PSD of discrete‐time random processes
Spectral lines in the PSD
PSD through filtering
Minimum-phase spectral factorization
1.5.4 PSD of passband processes
PSD of in-phase and quadrature components
Cyclostationary processes
1.6 The autocorrelation matrix
Properties
Eigenvalues
Other properties
Eigenvalue analysis for Hermitian matrices
1.7 Examples of random processes
1.8 Matched filter
White noise case
1.9 Ergodic random processes
1.9.1 Mean value estimators
Rectangular window
Exponential filter
General window
1.9.2 Correlation estimators
Unbiased estimate
Biased estimate
1.9.3 Power spectral density estimators
Periodogram or instantaneous spectrum
Welch periodogram
Blackman and Tukey correlogram
Windowing and window closing
1.10 Parametric models of random processes
ARMA
MA
AR
Spectral factorization of AR models
Whitening filter
Relation between ARMA, MA, and AR models
1.10.1 Autocorrelation of AR processes
1.10.2 Spectral estimation of an AR process
Some useful relations
AR model of sinusoidal processes
1.11 Guide to the bibliography
Bibliography
Appendix 1.A Multirate systems
1.A.1 Fundamentals
1.A.2 Decimation
1.A.3 Interpolation
1.A.4 Decimator filter
1.A.5 Interpolator filter
1.A.6 Rate conversion
1.A.7 Time interpolation
Linear interpolation
Quadratic interpolation
1.A.8 The noble identities
1.A.9 The polyphase representation
Efficient implementations
1.B Generation of a complex Gaussian noise
1.C Pseudo‐noise sequences
Maximal-length
CAZAC
Gold
Chapter 2 The Wiener filter
2.1 The Wiener filter
Matrix formulation
Optimum filter design
The principle of orthogonality
Expression of the minimum mean-square error
Characterization of the cost function surface
The Wiener filter in the z-domain
2.2 Linear prediction
Forward linear predictor
Optimum predictor coefficients
Forward prediction error filter
Relation between linear prediction and AR models
First- and second-order solutions
2.3 The least squares method
Data windowing
Matrix formulation
Correlation matrix
Determination of the optimum filter coefficients
2.3.1 The principle of orthogonality
Minimum cost function
The normal equation using the data matrix
Geometric interpretation: the projection operator
2.3.2 Solutions to the LS problem
Singular value decomposition
Minimum norm solution
2.4 The estimation problem
Estimation of a random variable
MMSE estimation
Extension to multiple observations
Linear MMSE estimation of a random variable
Linear MMSE estimation of a random vector
2.4.1 The Cramér–Rao lower bound
Extension to vector parameter
2.5 Examples of application
2.5.1 Identification of a linear discrete‐time system
2.5.2 Identification of a continuous‐time system
2.5.3 Cancellation of an interfering signal
2.5.4 Cancellation of a sinusoidal interferer with known frequency
2.5.5 Echo cancellation in digital subscriber loops
2.5.6 Cancellation of a periodic interferer
Bibliography
Appendix 2.A The Levinson–Durbin algorithm
Lattice filters
The Delsarte–Genin algorithm
Chapter 3 Adaptive transversal filters
3.1 The MSE design criterion
3.1.1 The steepest descent or gradient algorithm
Stability
Conditions for convergence
Adaptation gain
Transient behaviour of the MSE
3.1.2 The least mean square algorithm
Implementation
Computational complexity
Conditions for convergence
3.1.3 Convergence analysis of the LMS algorithm
Convergence of the mean
Convergence in the mean-square sense: real scalar case
Convergence in the mean-square sense: general case
Fundamental results
Observations
Final remarks
3.1.4 Other versions of the LMS algorithm
Leaky LMS
Sign algorithm
Normalized LMS
Variable adaptation gain
3.1.5 Example of application: the predictor
3.2 The recursive least squares algorithm
Normal equation
Derivation
Initialization
Recursive form of the minimum cost function
Convergence
Computational complexity
Example of application: the predictor
3.3 Fast recursive algorithms
3.3.1 Comparison of the various algorithms
3.4 Examples of application
3.4.1 Identification of a linear discrete‐time system
Finite alphabet case
3.4.2 Cancellation of a sinusoidal interferer with known frequency
Bibliography
Chapter 4 Transmission channels
4.1 Radio channel
4.1.1 Propagation and used frequencies in radio transmission
Basic propagation mechanisms
Frequency ranges
4.1.2 Analog front‐end architectures
Radiation masks
Conventional superheterodyne receiver
Alternative architectures
Direct conversion receiver
Single conversion to low-IF
Double conversion and wideband IF
4.1.3 General channel model
High power amplifier
Transmission medium
Additive noise
Phase noise
4.1.4 Narrowband radio channel model
Equivalent circuit at the receiver
Multipath
Path loss as a function of distance
4.1.5 Fading effects in propagation models
Macroscopic fading or shadowing
Microscopic fading
4.1.6 Doppler shift
4.1.7 Wideband channel model
Multipath channel parameters
Statistical description of fading channels
4.1.8 Channel statistics
Power delay profile
Coherence bandwidth
Doppler spectrum
Coherence time
Doppler spectrum models
Power angular spectrum
Coherence distance
On fading
4.1.9 Discrete‐time model for fading channels
Generation of a process with a pre-assigned spectrum
4.1.10 Discrete‐space model of shadowing
4.1.11 Multiantenna systems
Line of sight
Discrete-time model
Small number of scatterers
Large number of scatterers
4.2 Telephone channel
4.2.1 Distortion
4.2.2 Noise sources
Quantization noise:
Thermal noise:
4.2.3 Echo
Bibliography
Appendix 4.A Discrete‐time NB model for mmWave channels
4.A.1 Angular domain representation
Chapter 5 Vector quantization
5.1 Basic concept
5.2 Characterization of VQ
Parameters determining VQ performance
Comparison between VQ and scalar quantization
5.3 Optimum quantization
Generalized Lloyd algorithm
5.4 The Linde, Buzo, and Gray algorithm
5.4.1 k‐means clustering
Choice of the initial codebook
Splitting procedure
Selection of the training sequence
5.5 Variants of VQ
Tree search VQ
Multistage VQ
Product code VQ
5.6 VQ of channel state information
MISO channel quantization
Channel feedback with feedforward information
5.7 Principal component analysis
5.7.1 PCA and k‐means clustering
Bibliography
Chapter 6 Digital transmission model and channel capacity
6.1 Digital transmission model
6.2 Detection
6.2.1 Optimum detection
ML
MAP
6.2.2 Soft detection
LLRs associated to bits of BMAP
Simplified expressions
6.2.3 Receiver strategies
6.3 Relevant parameters of the digital transmission model
Relations among parameters
6.4 Error probability
6.5 Capacity
6.5.1 Discrete‐time AWGN channel
6.5.2 SISO narrowband AWGN channel
Channel gain
6.5.3 SISO dispersive AGN channel
6.5.4 MIMO discrete‐time NB AWGN channel
Continuous-time model
MIMO dispersive channel
6.6 Achievable rates of modulations in AWGN channels
6.6.1 Rate as a function of the SNR per dimension
6.6.2 Coding strategies depending on the signal‐to‐noise ratio
Coding gain
6.6.3 Achievable rate of an AWGN channel using PAM
Bibliography
Appendix 6.A Gray labelling
Appendix 6.B The Gaussian distribution and Marcum functions
6.B.1 The Q function
6.B.2 Marcum function
Chapter 7 Single‐carrier modulation
7.1 Signals and systems
7.1.1 Baseband digital transmission (PAM)
Modulator
Transmission channel
Receiver
Power spectral density
7.1.2 Passband digital transmission (QAM)
Modulator
Power spectral density
Three equivalent representations of the modulator
Coherent receiver
7.1.3 Baseband equivalent model of a QAM system
Signal analysis
7.1.4 Characterization of system elements
Transmitter
Transmission channel
Receiver
7.2 Intersymbol interference
Discrete-time equivalent system
Nyquist pulses
Eye diagram
7.3 Performance analysis
Signal-to-noise ratio
Symbol error probability in the absence of ISI
Matched filter receiver
7.4 Channel equalization
7.4.1 Zero‐forcing equalizer
7.4.2 Linear equalizer
Optimum receiver in the presence of noise and ISI
Alternative derivation of the IIR equalizer
Signal-to-noise ratio at
7.4.3 LE with a finite number of coefficients
Adaptive LE
7.4.4 Decision feedback equalizer
Design of a DFE with a finite number of coefficients
Design of a fractionally spaced DFE
Signal-to-noise ratio at the decision point
Remarks
7.4.5 Frequency domain equalization
DFE with data frame using a unique word
7.4.6 LE‐ZF
7.4.7 DFE‐ZF with IIR filters
DFE-ZF as noise predictor
DFE as ISI and noise predictor
7.4.8 Benchmark performance of LE‐ZF and DFE‐ZF
Comparison
Performance for two channel models
7.4.9 Passband equalizers
Passband receiver structure
Optimization of equalizer coefficients and carrier phase offset
Adaptive method
7.5 Optimum methods for data detection
Maximum a posteriori probability (MAP) criterion
7.5.1 Maximum‐likelihood sequence detection
Lower bound to error probability using MLSD
The Viterbi algorithm
Computational complexity of the VA
7.5.2 Maximum a posteriori probability detector
Statistical description of a sequential machine
The forward–backward algorithm
Scaling
The log likelihood function and the Max-Log-MAP criterion
LLRs associated to bits of BMAP
Relation between Max-Log–MAP and Log–MAP
7.5.3 Optimum receivers
7.5.4 The Ungerboeck's formulation of MLSD
7.5.5 Error probability achieved by MLSD
Computation of the minimum distance
7.5.6 The reduced‐state sequence detection
Trellis diagram
The RSSE algorithm
Further simplification: DFSE
7.6 Numerical results obtained by simulations
QPSK over a minimum-phase channel
QPSK over a non-minimum phase channel
8-PSK over a minimum phase channel
8-PSK over a non-minimum phase channel
7.7 Precoding for dispersive channels
7.7.1 Tomlinson–Harashima precoding
7.7.2 Flexible precoding
7.8 Channel estimation
7.8.1 The correlation method
7.8.2 The LS method
Formulation using the data matrix
7.8.3 Signal‐to‐estimation error ratio
Computation of the signal-to-estimation error ratio
On the selection of the channel length
7.8.4 Channel estimation for multirate systems
7.8.5 The LMMSE method
7.9 Faster‐than‐Nyquist Signalling
Bibliography
Appendix 7.A Simulation of a QAM system
Appendix 7.B Description of a finite‐state machine
Appendix 7.C Line codes for PAM systems
7.C.1 Line codes
Non-return-to-zero format
Return-to-zero format
Biphase format
Delay modulation or Miller code
Block line codes
Alternate mark inversion
7.C.2 Partial response systems
Appendix 7.D Implementation of a QAM transmitter
Chapter 8 Multicarrier modulation
8.1 MC systems
8.2 Orthogonality conditions
Time domain
Frequency domain
z-Transform domain
8.3 Efficient implementation of MC systems
MC implementation employing matched filters
Orthogonality conditions in terms of the polyphase components
MC implementation employing a prototype filter
8.4 Non‐critically sampled filter banks
8.5 Examples of MC systems
OFDM or DMT
Filtered multitone
8.6 Analog signal processing requirements in MC systems
8.6.1 Analog filter requirements
Interpolator filter and virtual subchannels
Modulator filter
8.6.2 Power amplifier requirements
8.7 Equalization
8.7.1 OFDM equalization
8.7.2 FMT equalization
Per-subchannel fractionally spaced equalization
Per-subchannel T-spaced equalization
Alternative per-subchannel T-spaced equalization
8.8 Orthogonal time frequency space modulation
OTFS equalization
8.9 Channel estimation in OFDM
Instantaneous estimate or LS method
LMMSE
The LS estimate with truncated impulse response
8.9.1 Channel estimate and pilot symbols
8.10 Multiuser access schemes
8.10.1 OFDMA
8.10.2 SC‐FDMA or DFT‐spread OFDM
8.11 Comparison between MC and SC systems
8.12 Other MC waveforms
Bibliography
Chapter 9 Transmission over multiple input multiple output channels
9.1 The MIMO NB channel
Spatial multiplexing and spatial diversity
Interference in MIMO channels
9.2 CSI only at the receiver
9.2.1 SIMO combiner
Equalization and diversity
9.2.2 MIMO combiner
Zero-forcing
MMSE
9.2.3 MIMO non‐linear detection and decoding
V-BLAST system
Spatial modulation
9.2.4 Space‐time coding
The Alamouti code
The Golden code
9.2.5 MIMO channel estimation
The least squares method
The LMMSE method
9.3 CSI only at the transmitter
9.3.1 MISO linear precoding
MISO antenna selection
9.3.2 MIMO linear precoding
ZF precoding
9.3.3 MIMO non‐linear precoding
Dirty paper coding
TH precoding
9.3.4 Channel estimation for CSIT
9.4 CSI at both the transmitter and the receiver
9.5 Hybrid beamforming
Hybrid beamforming and angular domain representation
9.6 Multiuser MIMO: broadcast channel
CSI only at the receivers
CSI only at the transmitter
9.6.1 CSI at both the transmitter and the receivers
Block diagonalization
User selection
Joint spatial division and multiplexing
9.6.2 Broadcast channel estimation
9.7 Multiuser MIMO: multiple‐access channel
CSI only at the transmitters
CSI only at the receiver
9.7.1 CSI at both the transmitters and the receiver
Block diagonalization
9.7.2 Multiple‐access channel estimation
9.8 Massive MIMO
9.8.1 Channel hardening
9.8.2 Multiuser channel orthogonality
Bibliography
Chapter 10 Spread‐spectrum systems
10.1 Spread‐spectrum techniques
10.1.1 Direct sequence systems
Classification of CDMA systems
Synchronization
10.1.2 Frequency hopping systems
Classification of FH systems
10.2 Applications of spread‐spectrum systems
10.2.1 Anti‐jamming
10.2.2 Multiple access
10.2.3 Interference rejection
10.3 Chip matched filter and rake receiver
Number of resolvable rays in a multipath channel
Chip matched filter
10.4 Interference
Detection strategies for multiple-access systems
10.5 Single‐user detection
Chip equalizer
Symbol equalizer
10.6 Multiuser detection
10.6.1 Block equalizer
10.6.2 Interference cancellation detector
Successive interference cancellation
Parallel interference cancellation
10.6.3 ML multiuser detector
Correlation matrix
Whitening filter
10.7 Multicarrier CDMA systems
Bibliography
Appendix 10.A Walsh Codes
Chapter 11 Channel codes
11.1 System model
11.2 Block codes
11.2.1 Theory of binary codes with group structure
Properties
Parity check matrix
Code generator matrix
Decoding of binary parity check codes
Cosets
Two conceptually simple decoding methods
Syndrome decoding
11.2.2 Fundamentals of algebra
modulo-q arithmetic
Polynomials with coefficients from a field
Modular arithmetic for polynomials
Remarks on finite fields
Roots of a polynomial
Minimum function
Methods to determine the minimum function
Properties of the minimum function
11.2.3 Cyclic codes
The algebra of cyclic codes
Properties of cyclic codes
Encoding by a shift register of length r
Encoding by a shift register of length k
Hard decoding of cyclic codes
Hamming codes
Burst error detection
11.2.4 Simplex cyclic codes
Property
Relation to PN sequences
11.2.5 BCH codes
An alternative method to specify the code polynomials
Bose-Chaudhuri–Hocquenhem codes
Binary BCH codes
Reed–Solomon codes
Decoding of BCH codes
Efficient decoding of BCH codes
11.2.6 Performance of block codes
11.3 Convolutional codes
11.3.1 General description of convolutional codes
Parity check matrix
Generator matrix
Transfer function
Catastrophic error propagation
11.3.2 Decoding of convolutional codes
Interleaving
Two decoding models
Decoding by the Viterbi algorithm
Decoding by the forward-backward algorithm
Sequential decoding
11.3.3 Performance of convolutional codes
11.4 Puncturing
11.5 Concatenated codes
The soft-output Viterbi algorithm
11.6 Turbo codes
Encoding
The basic principle of iterative decoding
FBA revisited
Iterative decoding
Performance evaluation
11.7 Iterative detection and decoding
11.8 Low‐density parity check codes
11.8.1 Representation of LDPC codes
Matrix representation
Graphical representation
11.8.2 Encoding
Encoding procedure
11.8.3 Decoding
Hard decision decoder
The sum-product algorithm decoder
The LR-SPA decoder
The LLR-SPA or log-domain SPA
The min-sum decoder
Other decoding algorithms
11.8.4 Example of application
Performance and coding gain
11.8.5 Comparison with turbo codes
11.9 Polar codes
11.9.1 Encoding
Internal CRC
LLRs associated to code bits
11.9.2 Tanner graph
11.9.3 Decoding algorithms
Successive cancellation decoding – the principle
Successive cancellation decoding – the algorithm
Successive cancellation list decoding
Other decoding algorithms
11.9.4 Frozen set design
Genie-aided SC decoding
Design based on density evolution
Channel polarization
11.9.5 Puncturing and shortening
Puncturing
Shortening
11.9.6 Performance
11.10 Milestones in channel coding
Bibliography
Appendix 11.A Non‐binary parity check codes
Linear codes
Parity check matrix
Code generator matrix
Decoding of non-binary parity check codes
Coset
Two conceptually simple decoding methods
Syndrome decoding
Chapter 12 Trellis coded modulation
12.1 Linear TCM for one‐ and two‐dimensional signal sets
12.1.1 Fundamental elements
Basic TCM scheme
Example
12.1.2 Set partitioning
12.1.3 Lattices
12.1.4 Assignment of symbols to the transitions in the trellis
12.1.5 General structure of the encoder/bit‐mapper
Computation of dfree
12.2 Multidimensional TCM
Encoding
Decoding
12.3 Rotationally invariant TCM schemes
Bibliography
Chapter 13 Techniques to achieve capacity
13.1 Capacity achieving solutions for multicarrier systems
13.1.1 Achievable bit rate of OFDM
13.1.2 Waterfilling solution
Iterative solution
13.1.3 Achievable rate under practical constraints
Effective SNR and system margin in MC systems
Uniform power allocation and minimum rate per subchannel
13.1.4 The bit and power loading problem revisited
Transmission modes
Problem formulation
Some simplifying assumptions
On loading algorithms
The Hughes-Hartogs algorithm
The Krongold–Ramchandran–Jones algorithm
The Chow–Cioffi–Bingham algorithm
Comparison
13.2 Capacity achieving solutions for single carrier systems
Achieving capacity
Bibliography
Chapter 14 Synchronization
14.1 The problem of synchronization for QAM systems
14.2 The phase‐locked loop
14.2.1 PLL baseband model
Linear approximation
14.2.2 Analysis of the PLL in the presence of additive noise
Noise analysis using the linearity assumption
14.2.3 Analysis of a second‐order PLL
14.3 Costas loop
14.3.1 PAM signals
14.3.2 QAM signals
14.4 The optimum receiver
Timing recovery
Carrier phase recovery
14.5 Algorithms for timing and carrier phase recovery
14.5.1 ML criterion
Assumption of slow time varying channel
14.5.2 Taxonomy of algorithms using the ML criterion
Feedback estimators
Early-late estimators
14.5.3 Timing estimators
Non-data aided
Data aided and data directed
Data and phase directed with feedback: differentiator scheme
Data and phase directed with feedback: Mueller and Muller scheme
Non-data aided with feedback
14.5.4 Phasor estimators
Data and timing directed
Non-data aided for M-PSK signals
Data and timing directed with feedback
14.6 Algorithms for carrier frequency recovery
14.6.1 Frequency offset estimators
Non-data aided
Non-data aided and timing independent with feedback
Non-data aided and timing directed with feedback
14.6.2 Estimators operating at the modulation rate
Data aided and data directed
Non-data aided for M-PSK
14.7 Second‐order digital PLL
14.8 Synchronization in spread‐spectrum systems
14.8.1 The transmission system
Transmitter
Optimum receiver
14.8.2 Timing estimators with feedback
Non-data aided: non-coherent DLL
Non-data aided modified code tracking loop
Data and phase directed: coherent DLL
14.9 Synchronization in OFDM
14.9.1 Frame synchronization
Effects of STO
Schmidl and Cox algorithm
14.9.2 Carrier frequency synchronization
Estimator performance
Other synchronization solutions
14.10 Synchronization in SC‐FDMA
Bibliography
Chapter 15 Self‐training equalization
15.1 Problem definition and fundamentals
Minimization of a special function
15.2 Three algorithms for PAM systems
The Sato algorithm
Benveniste–Goursat algorithm
Stop-and-go algorithm
Remarks
15.3 The contour algorithm for PAM systems
Simplified realization of the contour algorithm
15.4 Self‐training equalization for partial response systems
The Sato algorithm
The contour algorithm
15.5 Self‐training equalization for QAM systems
The Sato algorithm
15.5.1 Constant‐modulus algorithm
The contour algorithm
Joint contour algorithm and carrier phase tracking
15.6 Examples of applications
Bibliography
Appendix 15.A On the convergence of the contour algorithm
Chapter 16 Low‐complexity demodulators
16.1 Phase‐shift keying
16.1.1 Differential PSK
Error probability of M-DPSK
16.1.2 Differential encoding and coherent demodulation
Differentially encoded BPSK
Multilevel case
16.2 (D)PSK non‐coherent receivers
16.2.1 Baseband differential detector
16.2.2 IF‐band (1 bit) differential detector
Signal at detection point
16.2.3 FM discriminator with integrate and dump filter
16.3 Optimum receivers for signals with random phase
ML criterion
Implementation of a non-coherent ML receiver
Error probability for a non-coherent binary FSK system
Performance comparison of binary systems
16.4 Frequency‐based modulations
16.4.1 Frequency shift keying
Coherent demodulator
Non-coherent demodulator
Limiter–discriminator FM demodulator
16.4.2 Minimum‐shift keying
16.4.3 Remarks on spectral containment
16.5 Gaussian MSK
PSD of GMSK
16.5.1 Implementation of a GMSK scheme
Configuration I
Configuration II
Configuration III
16.5.2 Linear approximation of a GMSK signal
Performance of GMSK
Performance in the presence of multipath
Bibliography
Appendix 16.A Continuous phase modulation
Alternative definition of CPM
Advantages of CPM
Chapter 17 Applications of interference cancellation
17.1 Echo and near‐end crosstalk cancellation for PAM systems
Crosstalk cancellation and full-duplex transmission
Polyphase structure of the canceller
Canceller at symbol rate
Adaptive canceller
Canceller structure with distributed arithmetic
17.2 Echo cancellation for QAM systems
17.3 Echo cancellation for OFDM systems
17.4 Multiuser detection for VDSL
17.4.1 Upstream power back‐off
17.4.2 Comparison of PBO methods
Bibliography
Chapter 18 Examples of communication systems
18.1 The 5G cellular system
18.1.1 Cells in a wireless system
18.1.2 The release 15 of the 3GPP standard
18.1.3 Radio access network
Time-frequency plan
NR data transmission chain
OFDM numerology
Channel estimation
18.1.4 Downlink
Synchronization
Initial access or beam sweeping
Channel estimation
Channel state information reporting
18.1.5 Uplink
Transform precoding numerology
Channel estimation
Synchronization
Timing advance
18.1.6 Network slicing
18.2 GSM
Radio subsystem
18.3 Wireless local area networks
Medium access control protocols
18.4 DECT
18.5 Bluetooth
18.6 Transmission over unshielded twisted pairs
18.6.1 Transmission over UTP in the customer service area
18.6.2 High‐speed transmission over UTP in local area networks
18.7 Hybrid fibre/coaxial cable networks
Ranging and power adjustment in OFDMA systems
Ranging and power adjustment for uplink transmission
Bibliography
Appendix 18.A Duplexing
Three methods
Appendix 18.B Deterministic access methods
Chapter 19 High‐speed communications over twisted‐pair cables
19.1 Quaternary partial response class‐IV system
Analog filter design
Received signal and adaptive gain control
Near-end crosstalk cancellation
Decorrelation filter
Adaptive equalizer
Compensation of the timing phase drift
Adaptive equalizer coefficient adaptation
Convergence behaviour of the various algorithms
19.1.1 VLSI implementation
Adaptive digital NEXT canceller
Adaptive digital equalizer
Timing control
Viterbi detector
19.2 Dual‐duplex system
Dual-duplex transmission
Physical layer control
Coding and decoding
19.2.1 Signal processing functions
The 100BASE-T2 transmitter
The 100BASE-T2 receiver
Computational complexity of digital receive filters
Bibliography
Appendix 19.A Interference suppression
Index
EULA