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Algorithms for Communications Systems and their Applications

; Giovanni Cherubini ; Stefano Tomasin

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. Les mer
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Om boka

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.

Fakta

Innholdsfortegnelse

Preface 3


Acknowledgments 3


1 Elements of signal theory 7


1.1 Continuous-time linear systems 7


1.2 Discrete-time linear systems 10


Discrete Fourier transform 13


The DFT operator 14


Circular and linear convolution via DFT 15


Convolution by the overlap-save method 17


IIR and FIR filters 19


1.3 Signal bandwidth 22


The sampling theorem 24


Heaviside conditions for the absence of signal distortion 26


1.4 Passband signals and systems 26


Complex representation 26


Relation between a signal and its complex representation 28


Baseband equivalent of a transformation 36


Envelope and instantaneous phase and frequency 37


1.5 Second-order analysis of random processes 38


1.5.1 Correlation 39


Properties of the autocorrelation function 40


1.5.2 Power spectral density 40


Spectral lines in the PSD 40


Cross power spectral density 42


Properties of the PSD 42


PSD through filtering 43


1.5.3 PSD of discrete-time random processes 43


Spectral lines in the PSD 44


PSD through filtering 45


Minimum-phase spectral factorization 46


1.5.4 PSD of passband processes 47


PSD of in-phase and quadrature components 47


Cyclostationary processes 50


1.6 The autocorrelation matrix 56


Properties 56


Eigenvalues 56


Other properties 57


Eigenvalue analysis for Hermitian matrices 58


1.7 Examples of random processes 60


1.8 Matched filter 66


White noise case 68


1.9 Ergodic random processes 69


1.9.1 Mean value estimators 71


Rectangular window 74


Exponential filter 74


General window 75


1.9.2 Correlation estimators 75


Unbiased estimate 76


Biased estimate 76


1.9.3 Power spectral density estimators 77


Periodogram or instantaneous spectrum 77


Welch periodogram 78


Blackman and Tukey correlogram 79


Windowing and window closing 79


1.10 Parametric models of random processes 82


ARMA 82


MA 84


AR 84


Spectral factorization of AR models 87


Whitening filter 87


Relation between ARMA, MA, and AR models 87


1.10.1 Autocorrelation of AR processes 89


1.10.2 Spectral estimation of an AR process 91


Some useful relations 92


AR model of sinusoidal processes 94


1.11 Guide to the bibliography 95


Bibliography 95


Appendixes 97


1.A Multirate systems 98


1.A.1 Fundamentals 98


1.A.2 Decimation 100


1.A.3 Interpolation 102


1.A.4 Decimator filter 104


1.A.5 Interpolator filter 105


1.A.6 Rate conversion 108


1.A.7 Time interpolation 109


Linear interpolation 110


Quadratic interpolation 112


1.A.8 The noble identities 112


1.A.9 The polyphase representation 113


Efficient implementations 114


1.B Generation of a complex Gaussian noise 121


1.C Pseudo-noise sequences 122


Maximal-length 122


CAZAC 124


Gold 125


2 The Wiener filter 129


2.1 The Wiener filter 129


Matrix formulation 130


Optimum filter design 132


The principle of orthogonality 134


Expression of the minimum mean-square error 135


Characterization of the cost function surface 136


The Wiener filter in the z-domain 137


2.2 Linear prediction 140


Forward linear predictor 141


Optimum predictor coefficients 141


Forward prediction error filter 142


Relation between linear prediction and AR models 143


First and second order solutions 144


2.3 The least squares method 145


Data windowing 146


Matrix formulation 146


Correlation matrix 147


Determination of the optimum filter coefficients 147


2.3.1 The principle of orthogonality 148


Minimum cost function 149


The normal equation using the data matrix 149


Geometric interpretation: the projection operator 150


2.3.2 Solutions to the LS problem 151


Singular value decomposition 152


Minimum norm solution 154


2.4 The estimation problem 155


Estimation of a random variable 155


MMSE estimation 155


Extension to multiple observations 157


Linear MMSE estimation of a random variable 158


Linear MMSE estimation of a random vector 158


2.4.1 The Cramer-Rao lower bound 160


Extension to vector parameter 162


2.5 Examples of application 164


2.5.1 Identification of a linear discrete-time system 164


2.5.2 Identification of a continuous-time system 166


2.5.3 Cancellation of an interfering signal 169


2.5.4 Cancellation of a sinusoidal interferer with known frequency 170


2.5.5 Echo cancellation in digital subscriber loops 171


2.5.6 Cancellation of a periodic interferer 172


Bibliography 173


Appendixes 174


2.A The Levinson-Durbin algorithm 175


Lattice filters 176


The Delsarte-Genin algorithm 177


3 Adaptive transversal filters 179


3.1 The MSE design criterion 180


3.1.1 The steepest descent or gradient algorithm 181


Stability 181


Conditions for convergence 183


Adaptation gain 184


Transient behaviour of the MSE 185


3.1.2 The least mean square algorithm 186


Implementation 187


Computational complexity 188


Conditions for convergence 188


3.1.3 Convergence analysis of the LMS algorithm 190


Convergence of the mean 191


Convergence in the mean-square sense: real scalar case 192


Convergence in the mean-square sense: general case 193


Fundamental results 196


Observations 197


Final remarks 199


3.1.4 Other versions of the LMS algorithm 199


Leaky LMS 199


Sign algorithm 200


Normalized LMS 200


Variable adaptation gain 201


3.1.5 Example of application: the predictor 202


3.2 The recursive least squares algorithm 208


Normal equation 209


Derivation 210


Initialization 212


Recursive form of the minimum cost function 212


Convergence 214


Computational complexity 214


Example of application: the predictor 215


3.3 Fast recursive algorithms 215


3.3.1 Comparison of the various algorithms 216


3.4 Examples of application 216


3.4.1 Identification of a linear discrete-time system 217


Finite alphabet case 219


3.4.2 Cancellation of a sinusoidal interferer with known frequency 220


Bibliography 221


4 Transmission channels 223


4.1 Radio channel 223


4.1.1 Propagation and used frequencies in radio transmission 224


Basic propagation mechanisms 224


Frequency ranges 224


4.1.2 Analog front-end architectures 226


Radiation masks 226


Conventional superheterodyne receiver 227


Alternative architectures 227


Direct conversion receiver 228


Single conversion to low-IF 229


Double conversion and wideband IF 229


4.1.3 General channel model 230


High power amplifier 230


Transmission medium 233


Additive noise 234


Phase noise 234


4.1.4 Narrowband radio channel model 235


Equivalent circuit at the receiver 237


Multipath 238


Path loss as a function of distance 240


4.1.5 Fading effects in propagation models 243


Macroscopic fading or shadowing 243


Microscopic fading 245


4.1.6 Doppler shift 245


4.1.7 Wideband channel model 247


Multipath channel parameters 249


Statistical description of fading channels 250


4.1.8 Channel statistics 252


Power delay profile 252


Coherence bandwidth 253


Doppler spectrum 254


Coherence time 255


Doppler spectrum models 256


Power angular spectrum 256


Coherence distance 256


On fading 257


4.1.9 Discrete-time model for fading channels 258


Generation of a process with a preassigned spectrum 259


4.1.10 Discrete-space model of shadowing 261


4.1.11 Multiantenna systems 264


Discrete-time model 266


4.2 Telephone channel 268


Distortion 270


Noise sources 270


Echo 270


Appendixes 272


4.A Discrete-time NB model for mmWave channels 273


Angular domain representation 273


Bibliography 274


5 Vector quantization 277


5.1 Basic concept 277


5.2 Characterization of VQ 278


Parameters determining VQ performance 278


Comparison between VQ and scalar quantization 280


5.3 Optimum quantization 281


Generalized Lloyd algorithm 282


5.4 The Linde, Buzo, and Gray algorithm 284


Choice of the initial codebook 285


Splitting procedure 286


Selection of the training sequence 287


5.4.1 k-means clustering 288


5.5 Variants of VQ 288


Tree search VQ 288


Multistage VQ 289


Product code VQ 291


5.6 VQ of channel state information 292


MISO channel quantization 292


Channel feedback with feedforward information 294


5.7 Principal component analysis 295


5.7.1 PCA and k-means clustering 297


Bibliography 299


6 Digital transmission model and channel capacity 301


6.1 Digital transmission model 301


6.2 Detection 305


6.2.1 Optimum detection 306


ML 307


MAP 307


6.2.2 Soft detection 309


LLRs associated to bits of BMAP 309


Simplified expressions 312


6.2.3 Receiver strategies 314


6.3 Relevant parameters of the digital transmission model 314


Relations among parameters 315


6.4 Error probability 317


6.5 Capacity 320


6.5.1 Discrete-time AWGN channel 321


6.5.2 SISO narrowband AWGN channel 322


6.5.3 SISO dispersive AGN channel 322


6.5.4 MIMO discrete-time NB AWGN channel 325


6.6 Achievable rates of modulations in AWGN channels 326


6.6.1 Rate as a function of the SNR per dimension 327


6.6.2 Coding strategies depending on the signal-to-noise ratio 329


Coding gain 330


6.6.3 Achievable rate of an AWGN channel using PAM 331


Bibliography 333


Appendixes 334


6.A Gray labelling 335


6.B The Gaussian distribution and Marcum functions 336


6.B.1 The Q function 336


6.B.2 Marcum function 338


7 Single-carrier modulation 341


7.1 Signals and systems 341


7.1.1 Baseband digital transmission (PAM) 341


Modulator 342


Transmission channel 343


Receiver 343


Power spectral density 344


7.1.2 Passband digital transmission (QAM) 346


Modulator 346


Power spectral density 347


Three equivalent representations of the modulator 348


Coherent receiver 349


7.1.3 Baseband equivalent model of a QAM system 349


Signal analysis 349


7.1.4 Characterization of system elements 353


Transmitter 353


Transmission channel 354


Receiver 355


7.2 Intersymbol interference 356


Discrete-time equivalent system 356


Nyquist pulses 357


Eye diagram 361


7.3 Performance analysis 365


Signal-to-noise ratio 365


Symbol error probability in the absence of ISI 366


Matched filter receiver 367


7.4 Channel equalization 367


7.4.1 Zero-forcing equalizer 367


7.4.2 Linear equalizer 368


Optimum receiver in the presence of noise and ISI 369


Alternative derivation of the IIR equalizer 370


Signal-to-noise ratio at detector 374


7.4.3 LE with a finite number of coefficients 375


Adaptive LE 376


Fractionally spaced equalizer 378


7.4.4 Decision feedback equalizer 381


Design of a DFE with a finite number of coefficients 384


Design of a fractionally spaced DFE 387


Signal-to-noise ratio at the decision point 389


Remarks 390


7.4.5 Frequency domain equalization 390


DFE with data frame using a unique word 390


7.4.6 LE-ZF 394


7.4.7 DFE-ZF with IIR filters 394


DFE-ZF as noise predictor 400


DFE as ISI and noise predictor 400


7.4.8 Benchmark performance of LE-ZF and DFE-ZF 402


Comparison 402


Performance for two channel models 403


7.4.9 Passband equalizers 404


Passband receiver structure 405


Optimization of equalizer coefficients and carrier phase offset 407


Adaptive method 408


7.5 Optimum methods for data detection 410


7.5.1 Maximum-likelihood sequence detection 412


Lower bound to error probability using MLSD 413


The Viterbi algorithm 414


Computational complexity of the VA 419


7.5.2 Maximum a posteriori probability detector 419


Statistical description of a sequential machine 420


The forward-backward algorithm 421


Scaling 425


The log likelihood function and the Max-Log-MAP criterion 426


LLRs associated to bits of BMAP 427


Relation between Max-Log-MAP and Log-MAP 428


7.5.3 Optimum receivers 428


7.5.4 The Ungerboeck's formulation of MLSD 430


7.5.5 Error probability achieved by MLSD 433


Computation of the minimum distance 437


7.5.6 The reduced-state sequence detection 441


Trellis diagram 442


The RSSE algorithm 444


Further simplification: DFSE 446


7.6 Numerical results obtained by simulations 447


QPSK over a minimum-phase channel 447


QPSK over a non minimum phase channel 448


8-PSK over a minimum phase channel 449


8-PSK over a non minimum phase channel 449


7.7 Precoding for dispersive channels 451


7.7.1 Tomlinson-Harashima precoding 452


7.7.2 Flexible precoding 454


7.8 Channel estimation 456


7.8.1 The correlation method 456


7.8.2 The LS method 458


Formulation using the data matrix 459


7.8.3 Signal-to-estimation error ratio 460


7.8.4 Channel estimation for multirate systems 464


7.8.5 The LMMSE method 465


7.9 Faster-than-Nyquist Signalling 467


Bibliography 467


Appendixes 470


7.A Simulation of a QAM system 471


7.B Description of a finite-state machine 477


7.C Line codes for PAM systems 478


7.C.1 Line codes 478


Non-return-to-zero format 478


Return-to-zero format 479


Biphase format 480


Delay modulation or Miller code 481


Block line codes 481


Alternate mark inversion 481


7.C.2 Partial response systems 482


The choice of the PR polynomial 485


Symbol detection and error probability 489


Precoding 491


Error probability with precoding 492


Alternative interpretation of PR systems 493


7.D Implementation of a QAM transmitter 497


8 Multicarrier modulation 499


8.1 MC systems 499


8.2 Orthogonality conditions 500


Time domain 501


Frequency domain 501


z-transform domain 501


8.3 Efficient implementation of MC systems 502


MC implementation employing matched filters 502


Orthogonality conditions in terms of the polyphase components 505


MC implementation employing a prototype filter 505


8.4 Non-critically sampled filter banks 510


8.5 Examples of MC systems 515


OFDM or DMT 515


Filtered multitone 516


8.6 Analog signal processing requirements in MC systems 517


8.6.1 Analog filter requirements 517


Interpolator filter and virtual subchannels 517


Modulator filter 519


8.6.2 Power amplifier requirements 520


8.7 Equalization 521


8.7.1 OFDM equalization 521


8.7.2 FMT equalization 524


Per-subchannel fractionally-spaced equalization 524


Per-subchannel T -spaced equalization 524


Alternative per-subchannel T -spaced equalization 525


8.8 Orthogonal time frequency space modulation 526


OTFS equalization 527


8.9 Channel estimation in OFDM 527


Instantaneous estimate or LS method 528


LMMSE 530


The LS estimate with truncated impulse response 531


8.9.1 Channel estimate and pilot symbols 532


8.10 Multiuser access schemes 532


8.10.1 OFDMA 533


8.10.2 SC-FDMA or DFT-spread OFDM 534


8.11 Comparison between MC and SC systems 535


8.12 Other MC waveforms 536


Bibliography 537


9 Transmission over multiple input multiple output channels 539


9.1 The MIMO NB channel 539


Spatial multiplexing and spatial diversity 544


Interference in MIMO channels 544


9.2 CSI only at the receiver 545


9.2.1 SIMO combiner 545


Equalization and diversity 548


9.2.2 MIMO combiner 548


Zero-forcing 549


MMSE 550


9.2.3 MIMO nonlinear detection and decoding 550


V-BLAST system 550


Spatial modulation 552


9.2.4 Space-time coding 553


The Alamouti code 553


The Golden code 555


9.2.5 MIMO channel estimation 556


The least squares method 556


The LMMSE method 557


9.3 CSI only at the transmitter 558


9.3.1 MISO linear precoding 558


MISO antenna selection 559


9.3.2 MIMO linear precoding 560


ZF precoding 561


9.3.3 MIMO nonlinear precoding 562


Dirty paper coding 562


TH precoding 564


9.3.4 Channel estimation for CSIT 564


9.4 CSI at both the transmitter and the receiver 565


9.5 Hybrid beamforming 566


Hybrid beamforming and angular domain representation 567


9.6 Multiuser MIMO: broadcast channel 568


9.6.1 CSI at both the transmitter and the receivers 569


Block diagonalization 570


User selection 571


Joint spatial division and multiplexing 572


9.6.2 Broadcast channel estimation 573


9.7 Multiuser MIMO: multiple-access channel 573


9.7.1 CSI at both the transmitters and the receiver 574


Block diagonalization 575


9.7.2 Multiple-access channel estimation 575


9.8 Massive MIMO 575


9.8.1 Channel hardening 576


9.8.2 Multiuser channel orthogonality 576


Bibliography 576


10 Spread-spectrum systems 581


10.1 Spread-spectrum techniques 581


10.1.1 Direct sequence systems 581


Classification of CDMA systems 589


Synchronization 590


10.1.2 Frequency hopping systems 590


Classification of FH systems 592


10.2 Applications of spread-spectrum systems 593


10.2.1 Anti-jamming 594


10.2.2 Multiple access 596


10.2.3 Interference rejection 597


10.3 Chip matched filter and rake receiver 597


Number of resolvable rays in a multipath channel 597


Chip matched filter 598


10.4 Interference 601


Detection strategies for multiple-access systems 603


10.5 Single-user detection 603


Chip equalizer 603


Symbol equalizer 605


10.6 Multiuser detection 606


10.6.1 Block equalizer 606


10.6.2 Interference cancellation detector 608


Successive interference cancellation 608


Parallel interference cancellation 610


10.6.3 ML multiuser detector 610


Correlation matrix 611


Whitening filter 611


10.7 Multicarrier CDMA systems 612


Bibliography 613


Appendixes 615


10.A Walsh codes 616


11 Channel codes 619


11.1 System model 620


11.2 Block codes 622


11.2.1 Theory of binary codes with group structure 622


Properties 622


Parity check matrix 625


Code generator matrix 628


Decoding of binary parity check codes 628


Cosets 629


Two conceptually simple decoding methods 630


Syndrome decoding 631


11.2.2 Fundamentals of algebra 633


modulo-q arithmetic 634


Polynomials with coefficients from a field 637


Modular arithmetic for polynomials 638


Devices to sum and multiply elements in a finite field 640


Remarks on finite fields 642


Roots of a polynomial 646


Minimum function 648


Methods to determine the minimum function 650


Properties of the minimum function 652


11.2.3 Cyclic codes 653


The algebra of cyclic codes 653


Properties of cyclic codes 654


Encoding by a shift register of length r 658


Encoding by a shift register of length k 661


Hard decoding of cyclic codes 662


Hamming codes 663


Burst error detection 666


11.2.4 Simplex cyclic codes 666


Relation to PN sequences 668


11.2.5 BCH codes 669


An alternative method to specify the code polynomials 669


Bose-Chaudhuri-Hocquenhemcodes 671


Binary BCH codes 674


Reed-Solomon codes 675


Decoding of BCH codes 676


Efficient decoding of BCH codes 681


11.2.6 Performance of block codes 689


11.3 Convolutional codes 690


11.3.1 General description of convolutional codes 693


Parity check matrix 695


Generator matrix 696


Transfer function 696


Catastrophic error propagation 700


11.3.2 Decoding of convolutional codes 702


Interleaving 702


Two decoding models 703


Decoding by the Viterbi algorithm 704


Decoding by the forward-backward algorithm 705


Sequential decoding 706


11.3.3 Performance of convolutional codes 710


11.4 Puncturing 711


11.5 Concatenated codes 711


The soft-output Viterbi algorithm 711


11.6 Turbo codes 713


Encoding 713


The basic principle of iterative decoding 718


FBA revisited 719


Iterative decoding 728


Performance evaluation 730


11.7 Iterative detection and decoding 730


11.8 Low-density parity check codes 734


11.8.1 Representation of LDPC codes 735


Matrix representation 735


Graphical representation 736


11.8.2 Encoding 737


Encoding procedure 737


11.8.3 Decoding 738


Hard decision decoder 738


The sum-product algorithm decoder 741


The LR-SPA decoder 744


The LLR-SPA or log-domain SPA decoder 745


The min-sum decoder 747


Other decoding algorithms 748


11.8.4 Example of application 748


Performance and coding gain 748


11.8.5 Comparison with turbo codes 749


11.9 Polar codes 751


11.9.1 Encoding 752


Internal CRC 753


LLRs associated to code bits 754


11.9.2 Tanner graph 755


11.9.3 Decoding algorithms 757


Successive cancellation decoding - the principle 758


Successive cancellation decoding - the algorithm 760


Successive cancellation list decoding 763


Other decoding algorithms 765


11.9.4 Frozen set design 765


Genie-aided SC decoding 766


Design based on density evolution 767


Channel polarisation 770


11.9.5 Puncturing and shortening 770


Puncturing 771


Shortening 772


Frozen set design 774


11.9.6 Performance 774


11.10Milestones in channel coding 775


Bibliography 775


Appendixes 781


11.A Nonbinary parity check codes 782


Linear codes 783


Parity check matrix 784


Code generator matrix 785


Decoding of nonbinary parity check codes 786


Coset 786


Two conceptually simple decoding methods 787


Syndrome decoding 787


12 Trellis coded modulation 789


12.1 Linear TCM for one and two-dimensional signal sets 790


12.1.1 Fundamental elements 790


Basic TCM scheme 792


Example 792


12.1.2 Set partitioning 795


12.1.3 Lattices 797


12.1.4 Assignment of symbols to the transitions in the trellis 802


12.1.5 General structure of the encoder/bit-mapper 807


Computation of dfree 809


12.2 Multidimensional TCM 811


Encoding 812


Decoding 815


12.3 Rotationally invariant TCM schemes 817


Bibliography 817


13 Techniques to achieve capacity 819


13.1 Capacity achieving solutions for multicarrier systems 819


13.1.1 Achievable bit rate of OFDM 819


13.1.2 Waterfilling solution 820


Iterative solution 821


13.1.3 Achievable rate under practical constraints 821


Effective SNR and system margin in MC systems 822


Uniform power allocation and minimum rate per subchannel 823


13.1.4 The bit and power loading problem revisited 824


Transmission modes 824


Problem formulation 825


Some simplifying assumptions 826


On loading algorithms 826


The Hughes-Hartogs algorithm 827


The Krongold-Ramchandran Jones algorithm 827


The Chow-Cioffi Bingham algorithm 830


Comparison 832


13.2 Capacity achieving solutions for single carrier systems 833


Achieving capacity 837


Bibliography 838


14 Synchronization 839


14.1 The problem of synchronization for QAM systems 839


14.2 The phase-locked loop 841


14.2.1 PLL baseband model 843


Linear approximation 844


14.2.2 Analysis of the PLL in the presence of additive noise 846


Noise analysis using the linearity assumption 847


14.2.3 Analysis of a second order PLL 848


14.3 Costas loop 852


14.3.1 PAM signals 852


14.3.2 QAM signals 854


14.4 The optimum receiver 856


Timing recovery 858


Carrier phase recovery 862


14.5 Algorithms for timing and carrier phase recovery 863


14.5.1 ML criterion 863


Assumption of slow time varying channel 863


14.5.2 Taxonomy of algorithms using the ML criterion 863


Feedback estimators 865


Early-late estimators 866


14.5.3 Timing estimators 867


Non data aided 867


NDA synchronization via spectral estimation 869


Data aided and data directed 871


Data and phase directed with feedback: differentiator scheme 874


Data and phase directed with feedback: Mueller & Muller scheme 874


Non data aided with feedback 877


14.5.4 Phasor estimators 878


Data and timing directed 878


Non data aided forM-PSK signals 878


Data and timing directed with feedback 879


14.6 Algorithms for carrier frequency recovery 880


14.6.1 Frequency offset estimators 881


Non data aided 881


Non data aided and timing independent with feedback 882


Non data aided and timing directed with feedback 883


14.6.2 Estimators operating at the modulation rate 883


Data aided and data directed 884


Non data aided forM-PSK 885


14.7 Second-order digital PLL 885


14.8 Synchronization in spread-spectrum systems 885


14.8.1 The transmission system 885


Transmitter 885


Optimum receiver 886


14.8.2 Timing estimators with feedback 887


Non data aided: non coherent DLL 888


Non data aided modified code tracking loop 888


Data and phase directed: coherent DLL 891


14.9 Synchronization in OFDM 891


14.9.1 Frame synchronization 891


Effects of STO 891


Schmidl and Cox algorithm 893


14.9.2 Carrier frequency synchronization 894


Estimator performance 895


Other synchronization solutions 895


14.10Synchronization in SC-FDMA 896


Bibliography 899


15 Self-training equalization 901


15.1 Problem definition and fundamentals 901


Minimization of a special function 904


15.2 Three algorithms for PAM systems 908


The Sato algorithm 908


Benveniste-Goursat algorithm 909


Stop-and-go algorithm 909


Remarks 910


15.3 The contour algorithm for PAM systems 910


Simplified realization of the contour algorithm 912


15.4 Self-training equalization for partial response systems 913


The Sato algorithm 914


The contour algorithm 915


15.5 Self-training equalization for QAM systems 917


The Sato algorithm 918


15.5.1 Constant-modulus algorithm 919


The contour algorithm 921


Joint contour algorithm and carrier phase tracking 922


15.6 Examples of applications 924


Bibliography 928


Appendixes 930


15.A On the convergence of the contour algorithm 931


16 Low-complexity demodulators 933


16.1 Phase-shift keying 933


16.1.1 Differential PSK 935


Error probability ofM-DPSK 936


16.1.2 Differential encoding and coherent demodulation 937


Differentially encoded BPSK 937


Multilevel case 938


16.2 (D)PSK non-coherent receivers 940


16.2.1 Baseband differential detector 940


16.2.2 IF-band (1 Bit) differential detector 942


Signal at detection point 944


16.2.3 FM discriminator with integrate and dump filter 945


16.3 Optimum receivers for signals with random phase 946


ML criterion 948


Implementation of a non coherentML receiver 951


Error probability for a non coherent binary FSK system 953


Performance comparison of binary systems 956


16.4 Frequency-based modulations 957


16.4.1 Frequency shift keying 957


Coherent demodulator 959


Non coherent demodulator 959


Limiter-discriminator FM demodulator 961


16.4.2 Minimum-shift keying 961


Power spectral density of CPFSK 963


Performance 963


MSK with differential precoding 967


16.4.3 Remarks on spectral containment 968


16.5 Gaussian MSK 968


PSD of GMSK 972


16.5.1 Implementation of a GMSK scheme 973


Configuration I 973


Configuration II 974


Configuration III 975


16.5.2 Linear approximation of a GMSK signal 977


Performance of GMSK 978


Performance in the presence of multipath 983


Bibliography 985


Appendixes 985


16.A Continuous phase modulation 986


Alternative definition of CPM 986


Advantages of CPM 988


17 Applications of interference cancellation 989


17.1 Echo and near-end crosstalk cancellation for PAM systems 990


Crosstalk cancellation and full duplex transmission 991


Polyphase structure of the canceller 992


Canceller at symbol rate 993


Adaptive canceller 994


Canceller structure with distributed arithmetic 995


17.2 Echo cancellation for QAM systems 998


17.3 Echo cancellation for OFDM systems 1001


17.4 Multiuser detection for VDSL 1004


17.4.1 Upstream power back-off 1009


17.4.2 Comparison of PBO methods 1011


Bibliography 1014


18 Examples of communication systems 1019


18.1 The 5G cellular system 1019


18.1.1 Cells in a wireless system 1019


18.1.2 The release 15 of the 3GPP standard 1020


18.1.3 Radio access network 1021


Time-frequency plan 1022


NR data transmission chain 1023


OFDM numerology 1023


Channel estimation 1024


18.1.4 Downlink 1024


Synchronization 1026


Initial access or beam sweeping 1027


Channel estimation 1028


Channel state information reporting 1028


18.1.5 Uplink 1029


Transform precoding numerology 1029


Channel estimation 1029


Synchronization 1030


Timing advance 1031


18.1.6 Network slicing 1031


18.2 GSM 1032


Radio subsystem 1034


18.3 Wireless local area networks 1036


Medium access control protocols 1036


18.4 DECT 1037


18.5 Bluetooth 1040


18.6 Transmission over unshielded twisted pairs 1041


18.6.1 Transmission over UTP in the customer service area 1041


18.6.2 High speed transmission over UTP in local area networks 1045


18.7 Hybrid fibre/coaxial cable networks 1048


Ranging and power adjustment in OFDMA systems 1051


Ranging and power adjustment for uplink transmission 1052


Bibliography 1053


Appendixes 1057


18.A Duplexing 1058


Three methods 1058


18.B Deterministic access methods 1059


19 High-speed communications over twisted-pair cables 1063


19.1 Quaternary partial response class-IV system 1063


Analog filter design 1064


Received signal and adaptive gain control 1064


Near-end crosstalk cancellation 1065


Decorrelation filter 1065


Adaptive equalizer 1065


Compensation of the timing phase drift 1066


Adaptive equalizer coefficient adaptation 1066


Convergence behaviour of the various algorithms 1067


19.1.1 VLSI implementation 1069


Adaptive digital NEXT canceller 1069


Adaptive digital equalizer 1071


Timing control 1075


Viterbi detector 1077


19.2 Dual duplex system 1077


Dual duplex transmission 1077


Physical layer control 1080


Coding and decoding 1080


19.2.1 Signal processing functions 1083


The 100BASE-T2 transmitter 1083


The 100BASE-T2 receiver 1084


Computational complexity of digital receive filters 1086


Bibliography 1087


Appendixes 1087


19.A Interference suppression 1088

Om forfatteren

This welcome second edition to the 2002 original presents the logical arithmetical or computational procedures within communications systems that will ensure the solution to various problems. The authors comprehensively introduce the theoretical elements which are at the basis of the field of algorithms for communications systems. Various applications of these algorithms are then illustrated with a focus on wired and wireless network access technologies. The updated applications will focus on 5G standards, and new material will include MIMO systems (Space-time block coding / Spatial multiplexing / Beamforming and interference management / Channel Estimation /mmWave Model); OFDM and SC-FDMA (Synchronization / Resource allocation (bit and power loading) / Filtered OFDM); Full Duplex Systems (Digital interference cancellation techniques).do-noise sequences 122


Maximal-length 122


CAZAC 124


Gold 125


2 The Wiener filter 129


2.1 The Wiener filter 129


Matrix formulation 130


Optimum filter design 132


The principle of orthogonality 134


Expression of the minimum mean-square error 135


Characterization of the cost function surface 136


The Wiener filter in the z-domain 137


2.2 Linear prediction 140


Forward linear predictor 141


Optimum predictor coefficients 141


Forward prediction error