Algorithms for communications systems and their applications

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Title Page
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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