Algorithms for Communications Systems and their Applications

Inbunden, Engelska, 2021

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The definitive guide to problem-solving in the design of communications systems In Algorithms for Communications Systems and their Applications, 2nd Edition, authors Benvenuto, Cherubini, and Tomasin have delivered the ultimate and practical guide to applying algorithms in communications systems. Written for researchers and professionals in the areas of digital communications, signal processing, and computer engineering, Algorithms for Communications Systems presents algorithmic and computational procedures within communications systems that overcome a wide range of problems facing system designers. New material in this fully updated edition includes: MIMO systems (Space-time block coding/Spatial multiplexing /Beamforming and interference management/Channel Estimation)OFDM and SC-FDMA (Synchronization/Resource allocation (bit and power loading)/Filtered OFDM)Improved radio channel model (Doppler and shadowing/mmWave)Polar codes (including practical decoding methods)5G systems (New Radio architecture/initial access for mmWave/physical channels)The book retains the essential coding and signal processing theoretical and operative elements expected from a classic text, further adopting the new radio of 5G systems as a case study to create the definitive guide to modern communications systems.

Produktinformation

Utgivningsdatum:2021-02-04
Mått:178 x 254 x 55 mm
Vikt:1 928 g
Format:Inbunden
Språk:Engelska
Antal sidor:960
Upplaga:2
Förlag:John Wiley & Sons Inc
ISBN:9781119567967

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Innehållsförteckning

Preface 3Acknowledgments 31 Elements of signal theory 71.1 Continuous-time linear systems 71.2 Discrete-time linear systems 10Discrete Fourier transform 13The DFT operator 14Circular and linear convolution via DFT 15Convolution by the overlap-save method 17IIR and FIR filters 191.3 Signal bandwidth 22The sampling theorem 24Heaviside conditions for the absence of signal distortion 261.4 Passband signals and systems 26Complex representation 26Relation between a signal and its complex representation 28Baseband equivalent of a transformation 36Envelope and instantaneous phase and frequency 371.5 Second-order analysis of random processes 381.5.1 Correlation 39Properties of the autocorrelation function 401.5.2 Power spectral density 40Spectral lines in the PSD 40Cross power spectral density 42Properties of the PSD 42PSD through filtering 431.5.3 PSD of discrete-time random processes 43Spectral lines in the PSD 44PSD through filtering 45Minimum-phase spectral factorization 461.5.4 PSD of passband processes 47PSD of in-phase and quadrature components 47Cyclostationary processes 501.6 The autocorrelation matrix 56Properties 56Eigenvalues 56Other properties 57Eigenvalue analysis for Hermitian matrices 581.7 Examples of random processes 601.8 Matched filter 66White noise case 681.9 Ergodic random processes 691.9.1 Mean value estimators 71Rectangular window 74Exponential filter 74General window 751.9.2 Correlation estimators 75Unbiased estimate 76Biased estimate 761.9.3 Power spectral density estimators 77Periodogram or instantaneous spectrum 77Welch periodogram 78Blackman and Tukey correlogram 79Windowing and window closing 791.10 Parametric models of random processes 82ARMA 82MA 84AR 84Spectral factorization of AR models 87Whitening filter 87Relation between ARMA, MA, and AR models 871.10.1 Autocorrelation of AR processes 891.10.2 Spectral estimation of an AR process 91Some useful relations 92AR model of sinusoidal processes 941.11 Guide to the bibliography 95Bibliography 95Appendixes 971.A Multirate systems 981.A.1 Fundamentals 981.A.2 Decimation 1001.A.3 Interpolation 1021.A.4 Decimator filter 1041.A.5 Interpolator filter 1051.A.6 Rate conversion 1081.A.7 Time interpolation 109Linear interpolation 110Quadratic interpolation 1121.A.8 The noble identities 1121.A.9 The polyphase representation 113Efficient implementations 1141.B Generation of a complex Gaussian noise 1211.C Pseudo-noise sequences 122Maximal-length 122CAZAC 124Gold 1252 The Wiener filter 1292.1 The Wiener filter 129Matrix formulation 130Optimum filter design 132The principle of orthogonality 134Expression of the minimum mean-square error 135Characterization of the cost function surface 136The Wiener filter in the z-domain 1372.2 Linear prediction 140Forward linear predictor 141Optimum predictor coefficients 141Forward prediction error filter 142Relation between linear prediction and AR models 143First and second order solutions 1442.3 The least squares method 145Data windowing 146Matrix formulation 146Correlation matrix 147Determination of the optimum filter coefficients 1472.3.1 The principle of orthogonality 148Minimum cost function 149The normal equation using the data matrix 149Geometric interpretation: the projection operator 1502.3.2 Solutions to the LS problem 151Singular value decomposition 152Minimum norm solution 1542.4 The estimation problem 155Estimation of a random variable 155MMSE estimation 155Extension to multiple observations 157Linear MMSE estimation of a random variable 158Linear MMSE estimation of a random vector 1582.4.1 The Cramér-Rao lower bound 160Extension to vector parameter 1622.5 Examples of application 1642.5.1 Identification of a linear discrete-time system 1642.5.2 Identification of a continuous-time system 1662.5.3 Cancellation of an interfering signal 1692.5.4 Cancellation of a sinusoidal interferer with known frequency 1702.5.5 Echo cancellation in digital subscriber loops 1712.5.6 Cancellation of a periodic interferer 172Bibliography 173Appendixes 1742.A The Levinson-Durbin algorithm 175Lattice filters 176The Delsarte-Genin algorithm 1773 Adaptive transversal filters 1793.1 The MSE design criterion 1803.1.1 The steepest descent or gradient algorithm 181Stability 181Conditions for convergence 183Adaptation gain 184Transient behaviour of the MSE 1853.1.2 The least mean square algorithm 186Implementation 187Computational complexity 188Conditions for convergence 1883.1.3 Convergence analysis of the LMS algorithm 190Convergence of the mean 191Convergence in the mean-square sense: real scalar case 192Convergence in the mean-square sense: general case 193Fundamental results 196Observations 197Final remarks 1993.1.4 Other versions of the LMS algorithm 199Leaky LMS 199Sign algorithm 200Normalized LMS 200Variable adaptation gain 2013.1.5 Example of application: the predictor 2023.2 The recursive least squares algorithm 208Normal equation 209Derivation 210Initialization 212Recursive form of the minimum cost function 212Convergence 214Computational complexity 214Example of application: the predictor 2153.3 Fast recursive algorithms 2153.3.1 Comparison of the various algorithms 2163.4 Examples of application 2163.4.1 Identification of a linear discrete-time system 217Finite alphabet case 2193.4.2 Cancellation of a sinusoidal interferer with known frequency 220Bibliography 2214 Transmission channels 2234.1 Radio channel 2234.1.1 Propagation and used frequencies in radio transmission 224Basic propagation mechanisms 224Frequency ranges 2244.1.2 Analog front-end architectures 226Radiation masks 226Conventional superheterodyne receiver 227Alternative architectures 227Direct conversion receiver 228Single conversion to low-IF 229Double conversion and wideband IF 2294.1.3 General channel model 230High power amplifier 230Transmission medium 233Additive noise 234Phase noise 2344.1.4 Narrowband radio channel model 235Equivalent circuit at the receiver 237Multipath 238Path loss as a function of distance 2404.1.5 Fading effects in propagation models 243Macroscopic fading or shadowing 243Microscopic fading 2454.1.6 Doppler shift 2454.1.7 Wideband channel model 247Multipath channel parameters 249Statistical description of fading channels 2504.1.8 Channel statistics 252Power delay profile 252Coherence bandwidth 253Doppler spectrum 254Coherence time 255Doppler spectrum models 256Power angular spectrum 256Coherence distance 256On fading 2574.1.9 Discrete-time model for fading channels 258Generation of a process with a preassigned spectrum 2594.1.10 Discrete-space model of shadowing 2614.1.11 Multiantenna systems 264Discrete-time model 2664.2 Telephone channel 268Distortion 270Noise sources 270Echo 270Appendixes 2724.A Discrete-time NB model for mmWave channels 273Angular domain representation 273Bibliography 2745 Vector quantization 2775.1 Basic concept 2775.2 Characterization of VQ 278Parameters determining VQ performance 278Comparison between VQ and scalar quantization 2805.3 Optimum quantization 281Generalized Lloyd algorithm 2825.4 The Linde, Buzo, and Gray algorithm 284Choice of the initial codebook 285Splitting procedure 286Selection of the training sequence 2875.4.1 k-means clustering 2885.5 Variants of VQ 288Tree search VQ 288Multistage VQ 289Product code VQ 2915.6 VQ of channel state information 292MISO channel quantization 292Channel feedback with feedforward information 2945.7 Principal component analysis 2955.7.1 PCA and k-means clustering 297Bibliography 2996 Digital transmission model and channel capacity 3016.1 Digital transmission model 3016.2 Detection 3056.2.1 Optimum detection 306ML 307MAP 3076.2.2 Soft detection 309LLRs associated to bits of BMAP 309Simplified expressions 3126.2.3 Receiver strategies 3146.3 Relevant parameters of the digital transmission model 314Relations among parameters 3156.4 Error probability 3176.5 Capacity 3206.5.1 Discrete-time AWGN channel 3216.5.2 SISO narrowband AWGN channel 3226.5.3 SISO dispersive AGN channel 3226.5.4 MIMO discrete-time NB AWGN channel 3256.6 Achievable rates of modulations in AWGN channels 3266.6.1 Rate as a function of the SNR per dimension 3276.6.2 Coding strategies depending on the signal-to-noise ratio 329Coding gain 3306.6.3 Achievable rate of an AWGN channel using PAM 331Bibliography 333Appendixes 3346.A Gray labelling 3356.B The Gaussian distribution and Marcum functions 3366.B.1 The Q function 3366.B.2 Marcum function 3387 Single-carrier modulation 3417.1 Signals and systems 3417.1.1 Baseband digital transmission (PAM) 341Modulator 342Transmission channel 343Receiver 343Power spectral density 3447.1.2 Passband digital transmission (QAM) 346Modulator 346Power spectral density 347Three equivalent representations of the modulator 348Coherent receiver 3497.1.3 Baseband equivalent model of a QAM system 349Signal analysis 3497.1.4 Characterization of system elements 353Transmitter 353Transmission channel 354Receiver 3557.2 Intersymbol interference 356Discrete-time equivalent system 356Nyquist pulses 357Eye diagram 3617.3 Performance analysis 365Signal-to-noise ratio 365Symbol error probability in the absence of ISI 366Matched filter receiver 3677.4 Channel equalization 3677.4.1 Zero-forcing equalizer 3677.4.2 Linear equalizer 368Optimum receiver in the presence of noise and ISI 369Alternative derivation of the IIR equalizer 370Signal-to-noise ratio at detector 3747.4.3 LE with a finite number of coefficients 375Adaptive LE 376Fractionally spaced equalizer 3787.4.4 Decision feedback equalizer 381Design of a DFE with a finite number of coefficients 384Design of a fractionally spaced DFE 387Signal-to-noise ratio at the decision point 389Remarks 3907.4.5 Frequency domain equalization 390DFE with data frame using a unique word 3907.4.6 LE-ZF 3947.4.7 DFE-ZF with IIR filters 394DFE-ZF as noise predictor 400DFE as ISI and noise predictor 4007.4.8 Benchmark performance of LE-ZF and DFE-ZF 402Comparison 402Performance for two channel models 4037.4.9 Passband equalizers 404Passband receiver structure 405Optimization of equalizer coefficients and carrier phase offset 407Adaptive method 4087.5 Optimum methods for data detection 4107.5.1 Maximum-likelihood sequence detection 412Lower bound to error probability using MLSD 413The Viterbi algorithm 414Computational complexity of the VA 4197.5.2 Maximum a posteriori probability detector 419Statistical description of a sequential machine 420The forward-backward algorithm 421Scaling 425The log likelihood function and the Max-Log-MAP criterion 426LLRs associated to bits of BMAP 427Relation between Max-Log-MAP and Log-MAP 4287.5.3 Optimum receivers 4287.5.4 The Ungerboeck’s formulation of MLSD 4307.5.5 Error probability achieved by MLSD 433Computation of the minimum distance 4377.5.6 The reduced-state sequence detection 441Trellis diagram 442The RSSE algorithm 444Further simplification: DFSE 4467.6 Numerical results obtained by simulations 447QPSK over a minimum-phase channel 447QPSK over a non minimum phase channel 4488-PSK over a minimum phase channel 4498-PSK over a non minimum phase channel 4497.7 Precoding for dispersive channels 4517.7.1 Tomlinson-Harashima precoding 4527.7.2 Flexible precoding 4547.8 Channel estimation 4567.8.1 The correlation method 4567.8.2 The LS method 458Formulation using the data matrix 4597.8.3 Signal-to-estimation error ratio 4607.8.4 Channel estimation for multirate systems 4647.8.5 The LMMSE method 4657.9 Faster-than-Nyquist Signalling 467Bibliography 467Appendixes 4707.A Simulation of a QAM system 4717.B Description of a finite-state machine 4777.C Line codes for PAM systems 4787.C.1 Line codes 478Non-return-to-zero format 478Return-to-zero format 479Biphase format 480Delay modulation or Miller code 481Block line codes 481Alternate mark inversion 4817.C.2 Partial response systems 482The choice of the PR polynomial 485Symbol detection and error probability 489Precoding 491Error probability with precoding 492Alternative interpretation of PR systems 4937.D Implementation of a QAM transmitter 4978 Multicarrier modulation 4998.1 MC systems 4998.2 Orthogonality conditions 500Time domain 501Frequency domain 501z-transform domain 5018.3 Efficient implementation of MC systems 502MC implementation employing matched filters 502Orthogonality conditions in terms of the polyphase components 505MC implementation employing a prototype filter 5058.4 Non-critically sampled filter banks 5108.5 Examples of MC systems 515OFDM or DMT 515Filtered multitone 5168.6 Analog signal processing requirements in MC systems 5178.6.1 Analog filter requirements 517Interpolator filter and virtual subchannels 517Modulator filter 5198.6.2 Power amplifier requirements 5208.7 Equalization 5218.7.1 OFDM equalization 5218.7.2 FMT equalization 524Per-subchannel fractionally-spaced equalization 524Per-subchannel T -spaced equalization 524Alternative per-subchannel T -spaced equalization 5258.8 Orthogonal time frequency space modulation 526OTFS equalization 5278.9 Channel estimation in OFDM 527Instantaneous estimate or LS method 528LMMSE 530The LS estimate with truncated impulse response 5318.9.1 Channel estimate and pilot symbols 5328.10 Multiuser access schemes 5328.10.1 OFDMA 5338.10.2 SC-FDMA or DFT-spread OFDM 5348.11 Comparison between MC and SC systems 5358.12 Other MC waveforms 536Bibliography 5379 Transmission over multiple input multiple output channels 5399.1 The MIMO NB channel 539Spatial multiplexing and spatial diversity 544Interference in MIMO channels 5449.2 CSI only at the receiver 5459.2.1 SIMO combiner 545Equalization and diversity 5489.2.2 MIMO combiner 548Zero-forcing 549MMSE 5509.2.3 MIMO nonlinear detection and decoding 550V-BLAST system 550Spatial modulation 5529.2.4 Space-time coding 553The Alamouti code 553The Golden code 5559.2.5 MIMO channel estimation 556The least squares method 556The LMMSE method 5579.3 CSI only at the transmitter 5589.3.1 MISO linear precoding 558MISO antenna selection 5599.3.2 MIMO linear precoding 560ZF precoding 5619.3.3 MIMO nonlinear precoding 562Dirty paper coding 562TH precoding 5649.3.4 Channel estimation for CSIT 5649.4 CSI at both the transmitter and the receiver 5659.5 Hybrid beamforming 566Hybrid beamforming and angular domain representation 5679.6 Multiuser MIMO: broadcast channel 5689.6.1 CSI at both the transmitter and the receivers 569Block diagonalization 570User selection 571Joint spatial division and multiplexing 5729.6.2 Broadcast channel estimation 5739.7 Multiuser MIMO: multiple-access channel 5739.7.1 CSI at both the transmitters and the receiver 574Block diagonalization 5759.7.2 Multiple-access channel estimation 5759.8 Massive MIMO 5759.8.1 Channel hardening 5769.8.2 Multiuser channel orthogonality 576Bibliography 57610 Spread-spectrum systems 58110.1 Spread-spectrum techniques 58110.1.1 Direct sequence systems 581Classification of CDMA systems 589Synchronization 59010.1.2 Frequency hopping systems 590Classification of FH systems 59210.2 Applications of spread-spectrum systems 59310.2.1 Anti-jamming 59410.2.2 Multiple access 59610.2.3 Interference rejection 59710.3 Chip matched filter and rake receiver 597Number of resolvable rays in a multipath channel 597Chip matched filter 59810.4 Interference 601Detection strategies for multiple-access systems 60310.5 Single-user detection 603Chip equalizer 603Symbol equalizer 60510.6 Multiuser detection 60610.6.1 Block equalizer 60610.6.2 Interference cancellation detector 608Successive interference cancellation 608Parallel interference cancellation 61010.6.3 ML multiuser detector 610Correlation matrix 611Whitening filter 61110.7 Multicarrier CDMA systems 612Bibliography 613Appendixes 61510.A Walsh codes 61611 Channel codes 61911.1 System model 62011.2 Block codes 62211.2.1 Theory of binary codes with group structure 622Properties 622Parity check matrix 625Code generator matrix 628Decoding of binary parity check codes 628Cosets 629Two conceptually simple decoding methods 630Syndrome decoding 63111.2.2 Fundamentals of algebra 633modulo-q arithmetic 634Polynomials with coefficients from a field 637Modular arithmetic for polynomials 638Devices to sum and multiply elements in a finite field 640Remarks on finite fields 642Roots of a polynomial 646Minimum function 648Methods to determine the minimum function 650Properties of the minimum function 65211.2.3 Cyclic codes 653The algebra of cyclic codes 653Properties of cyclic codes 654Encoding by a shift register of length r 658Encoding by a shift register of length k 661Hard decoding of cyclic codes 662Hamming codes 663Burst error detection 66611.2.4 Simplex cyclic codes 666Relation to PN sequences 66811.2.5 BCH codes 669An alternative method to specify the code polynomials 669Bose-Chaudhuri-Hocquenhemcodes 671Binary BCH codes 674Reed-Solomon codes 675Decoding of BCH codes 676Efficient decoding of BCH codes 68111.2.6 Performance of block codes 68911.3 Convolutional codes 69011.3.1 General description of convolutional codes 693Parity check matrix 695Generator matrix 696Transfer function 696Catastrophic error propagation 70011.3.2 Decoding of convolutional codes 702Interleaving 702Two decoding models 703Decoding by the Viterbi algorithm 704Decoding by the forward-backward algorithm 705Sequential decoding 70611.3.3 Performance of convolutional codes 71011.4 Puncturing 71111.5 Concatenated codes 711The soft-output Viterbi algorithm 71111.6 Turbo codes 713Encoding 713The basic principle of iterative decoding 718FBA revisited 719Iterative decoding 728Performance evaluation 73011.7 Iterative detection and decoding 73011.8 Low-density parity check codes 73411.8.1 Representation of LDPC codes 735Matrix representation 735Graphical representation 73611.8.2 Encoding 737Encoding procedure 73711.8.3 Decoding 738Hard decision decoder 738The sum-product algorithm decoder 741The LR-SPA decoder 744The LLR-SPA or log-domain SPA decoder 745The min-sum decoder 747Other decoding algorithms 74811.8.4 Example of application 748Performance and coding gain 74811.8.5 Comparison with turbo codes 74911.9 Polar codes 75111.9.1 Encoding 752Internal CRC 753LLRs associated to code bits 75411.9.2 Tanner graph 75511.9.3 Decoding algorithms 757Successive cancellation decoding - the principle 758Successive cancellation decoding - the algorithm 760Successive cancellation list decoding 763Other decoding algorithms 76511.9.4 Frozen set design 765Genie-aided SC decoding 766Design based on density evolution 767Channel polarisation 77011.9.5 Puncturing and shortening 770Puncturing 771Shortening 772Frozen set design 77411.9.6 Performance 77411.10Milestones in channel coding 775Bibliography 775Appendixes 78111.A Nonbinary parity check codes 782Linear codes 783Parity check matrix 784Code generator matrix 785Decoding of nonbinary parity check codes 786Coset 786Two conceptually simple decoding methods 787Syndrome decoding 78712 Trellis coded modulation 78912.1 Linear TCM for one and two-dimensional signal sets 79012.1.1 Fundamental elements 790Basic TCM scheme 792Example 79212.1.2 Set partitioning 79512.1.3 Lattices 79712.1.4 Assignment of symbols to the transitions in the trellis 80212.1.5 General structure of the encoder/bit-mapper 807Computation of dfree 80912.2 Multidimensional TCM 811Encoding 812Decoding 81512.3 Rotationally invariant TCM schemes 817Bibliography 81713 Techniques to achieve capacity 81913.1 Capacity achieving solutions for multicarrier systems 81913.1.1 Achievable bit rate of OFDM 81913.1.2 Waterfilling solution 820Iterative solution 82113.1.3 Achievable rate under practical constraints 821Effective SNR and system margin in MC systems 822Uniform power allocation and minimum rate per subchannel 82313.1.4 The bit and power loading problem revisited 824Transmission modes 824Problem formulation 825Some simplifying assumptions 826On loading algorithms 826The Hughes-Hartogs algorithm 827The Krongold-Ramchandran Jones algorithm 827The Chow-Cioffi Bingham algorithm 830Comparison 83213.2 Capacity achieving solutions for single carrier systems 833Achieving capacity 837Bibliography 83814 Synchronization 83914.1 The problem of synchronization for QAM systems 83914.2 The phase-locked loop 84114.2.1 PLL baseband model 843Linear approximation 84414.2.2 Analysis of the PLL in the presence of additive noise 846Noise analysis using the linearity assumption 84714.2.3 Analysis of a second order PLL 84814.3 Costas loop 85214.3.1 PAM signals 85214.3.2 QAM signals 85414.4 The optimum receiver 856Timing recovery 858Carrier phase recovery 86214.5 Algorithms for timing and carrier phase recovery 86314.5.1 ML criterion 863Assumption of slow time varying channel 86314.5.2 Taxonomy of algorithms using the ML criterion 863Feedback estimators 865Early-late estimators 86614.5.3 Timing estimators 867Non data aided 867NDA synchronization via spectral estimation 869Data aided and data directed 871Data and phase directed with feedback: differentiator scheme 874Data and phase directed with feedback: Mueller & Muller scheme 874Non data aided with feedback 87714.5.4 Phasor estimators 878Data and timing directed 878Non data aided forM-PSK signals 878Data and timing directed with feedback 87914.6 Algorithms for carrier frequency recovery 88014.6.1 Frequency offset estimators 881Non data aided 881Non data aided and timing independent with feedback 882Non data aided and timing directed with feedback 88314.6.2 Estimators operating at the modulation rate 883Data aided and data directed 884Non data aided forM-PSK 88514.7 Second-order digital PLL 88514.8 Synchronization in spread-spectrum systems 88514.8.1 The transmission system 885Transmitter 885Optimum receiver 88614.8.2 Timing estimators with feedback 887Non data aided: non coherent DLL 888Non data aided modified code tracking loop 888Data and phase directed: coherent DLL 89114.9 Synchronization in OFDM 89114.9.1 Frame synchronization 891Effects of STO 891Schmidl and Cox algorithm 89314.9.2 Carrier frequency synchronization 894Estimator performance 895Other synchronization solutions 89514.10Synchronization in SC-FDMA 896Bibliography 89915 Self-training equalization 90115.1 Problem definition and fundamentals 901Minimization of a special function 90415.2 Three algorithms for PAM systems 908The Sato algorithm 908Benveniste-Goursat algorithm 909Stop-and-go algorithm 909Remarks 91015.3 The contour algorithm for PAM systems 910Simplified realization of the contour algorithm 91215.4 Self-training equalization for partial response systems 913The Sato algorithm 914The contour algorithm 91515.5 Self-training equalization for QAM systems 917The Sato algorithm 91815.5.1 Constant-modulus algorithm 919The contour algorithm 921Joint contour algorithm and carrier phase tracking 92215.6 Examples of applications 924Bibliography 928Appendixes 93015.A On the convergence of the contour algorithm 93116 Low-complexity demodulators 93316.1 Phase-shift keying 93316.1.1 Differential PSK 935Error probability ofM-DPSK 93616.1.2 Differential encoding and coherent demodulation 937Differentially encoded BPSK 937Multilevel case 93816.2 (D)PSK non-coherent receivers 94016.2.1 Baseband differential detector 94016.2.2 IF-band (1 Bit) differential detector 942Signal at detection point 94416.2.3 FM discriminator with integrate and dump filter 94516.3 Optimum receivers for signals with random phase 946ML criterion 948Implementation of a non coherentML receiver 951Error probability for a non coherent binary FSK system 953Performance comparison of binary systems 95616.4 Frequency-based modulations 95716.4.1 Frequency shift keying 957Coherent demodulator 959Non coherent demodulator 959Limiter-discriminator FM demodulator 96116.4.2 Minimum-shift keying 961Power spectral density of CPFSK 963Performance 963MSK with differential precoding 96716.4.3 Remarks on spectral containment 96816.5 Gaussian MSK 968PSD of GMSK 97216.5.1 Implementation of a GMSK scheme 973Configuration I 973Configuration II 974Configuration III 97516.5.2 Linear approximation of a GMSK signal 977Performance of GMSK 978Performance in the presence of multipath 983Bibliography 985Appendixes 98516.A Continuous phase modulation 986Alternative definition of CPM 986Advantages of CPM 98817 Applications of interference cancellation 98917.1 Echo and near–end crosstalk cancellation for PAM systems 990Crosstalk cancellation and full duplex transmission 991Polyphase structure of the canceller 992Canceller at symbol rate 993Adaptive canceller 994Canceller structure with distributed arithmetic 99517.2 Echo cancellation for QAM systems 99817.3 Echo cancellation for OFDM systems 100117.4 Multiuser detection for VDSL 100417.4.1 Upstream power back-off 100917.4.2 Comparison of PBO methods 1011Bibliography 101418 Examples of communication systems 101918.1 The 5G cellular system 101918.1.1 Cells in a wireless system 101918.1.2 The release 15 of the 3GPP standard 102018.1.3 Radio access network 1021Time-frequency plan 1022NR data transmission chain 1023OFDM numerology 1023Channel estimation 102418.1.4 Downlink 1024Synchronization 1026Initial access or beam sweeping 1027Channel estimation 1028Channel state information reporting 102818.1.5 Uplink 1029Transform precoding numerology 1029Channel estimation 1029Synchronization 1030Timing advance 103118.1.6 Network slicing 103118.2 GSM 1032Radio subsystem 103418.3 Wireless local area networks 1036Medium access control protocols 103618.4 DECT 103718.5 Bluetooth 104018.6 Transmission over unshielded twisted pairs 104118.6.1 Transmission over UTP in the customer service area 104118.6.2 High speed transmission over UTP in local area networks 104518.7 Hybrid fibre/coaxial cable networks 1048Ranging and power adjustment in OFDMA systems 1051Ranging and power adjustment for uplink transmission 1052Bibliography 1053Appendixes 105718.A Duplexing 1058Three methods 105818.B Deterministic access methods 105919 High-speed communications over twisted-pair cables 106319.1 Quaternary partial response class-IV system 1063Analog filter design 1064Received signal and adaptive gain control 1064Near-end crosstalk cancellation 1065Decorrelation filter 1065Adaptive equalizer 1065Compensation of the timing phase drift 1066Adaptive equalizer coefficient adaptation 1066Convergence behaviour of the various algorithms 106719.1.1 VLSI implementation 1069Adaptive digital NEXT canceller 1069Adaptive digital equalizer 1071Timing control 1075Viterbi detector 107719.2 Dual duplex system 1077Dual duplex transmission 1077Physical layer control 1080Coding and decoding 108019.2.1 Signal processing functions 1083The 100BASE-T2 transmitter 1083The 100BASE-T2 receiver 1084Computational complexity of digital receive filters 1086Bibliography 1087Appendixes 108719.A Interference suppression 1088