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Digital Signal Processing for Communications and Information Systems (DSP-CIS)

Digital Signal Processing for Communications and Information Systems (DSP-CIS). Marc Moonen Dept. E.E./ESAT, K.U.Leuven marc.moonen@esat.kuleuven.be www.esat.kuleuven.be / scd /. Chapter-1 : Introduction. Part-1: Aims/Scope Why study DSP ?

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Digital Signal Processing for Communications and Information Systems (DSP-CIS)

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  1. Digital Signal Processingfor Communications and Information Systems(DSP-CIS) Marc Moonen Dept. E.E./ESAT, K.U.Leuven marc.moonen@esat.kuleuven.be www.esat.kuleuven.be/scd/

  2. Chapter-1 : Introduction • Part-1: • Aims/Scope Why study DSP ? DSP in applications : GSM example • Overview • Activities Lectures: Course Notes/Literature Exercise Sessions: Acoustic Modem Project Exam • Part-2: • Acoustic Modem Project

  3. Why study DSP ? • Analog Systems vs. Digital Systems - Can translate (any) analog (e.g. filter) design into digital - Going `digital’ allows to expand functionality/flexibility/… (e.g. how would you do analog speech recognition ? analog audio compression ? …? ) OUT OUT IN 2 IN +2 A/D D/A =4

  4. Why study DSP ? • Start with one `DSP in applications’ example: • DSP in mobile communications (GSM) • Main message: Consumer electronics products (and many other systems) have become ‘supercomputers’(Mops/sec…Gops/sec), packed with mathematics & DSP functionalities…

  5. DSP in applications : GSM Cellular mobile telephony (e.g. GSM) • Basic network architecture : -Country covered by a grid of cells -Each cell has a base station -Base station connected to land telephone network and communicates with mobiles via a radio interface -Digital communication format

  6. DSP in applications : GSM • DSP for digital communications (`physical layer’ ) : • A common misunderstanding is that digital communications is `simple’…. • While in practice… .99,.01,.96,.95,.07,… Transmitter 1,0,1,1,0,… Channel Receiver x + x decision 1,0,1,1,0,… 1/a a noise

  7. Transmitter 1,0,1,1,0,… Receiver `Multipath’ Channel ?? + noise DSP in applications : GSM • DSP for digital communications (`physical layer’ ) : • While in practice… • This calls for channel modeling + compensation (equalization) !! .59,.41,.76,.05,.37,… 1,0,1,1,0,…

  8. Δ Δ Δ Δ Δ Δ Δ Δ Δ Δ a `Multipath’ Channel b c ≈ + + d e DSP in applications : GSM • GSM Channel Estimation/Compensation • Multi-path channel is modeled with short (3…5 taps) FIR filter H(z)= a+b.zˉ¹+c.z ˉ²+d.z ˉ³+e.z ˉ(interpretation?) 4

  9. Δ Δ Δ Δ Δ Δ Δ Δ Δ Δ a b c + d e DSP in applications : GSM • GSM Channel Estimation/Compensation (continued) • Multi-path channel is modeled with short (3…5 taps) FIR filter H(z)= a+b.zˉ¹+c.z ˉ²+d.z ˉ³+e.z ˉ 4 OUT[k] IN[k] =convolution

  10. DSP in applications : GSM • GSM Channel Estimation/Compensation (continued) • Channel coefficients (a,b,c,d,e) are identified in receiver based on transmission of pre-defined training sequences (TS), in between data bits. Problem to be solved at receiver is: `given channel input (=TS) and channel output (=observed), compute channel coefficients’ This leads to a least-squares parameter estimation See PART-III on ‘Optimal Filtering’ Carl Friedrich Gauss (1777 – 1855)

  11. DSP in applications : GSM • GSM Channel Estimation/Compensation (continued) • Channel coefficients (cfr. a,b,c,d,e) are identified in receiver based on transmission of pre-defined training sequences (TS), in between data bits. • Channel model is then used to design suitable equalizer (`channel inversion’), or (better) to reconstruct transmitted data bits based on maximum-likelihood sequence estimation (e.g. `Viterbi decoding’). • Channel is highly time-varying (e.g. terminal speed 120 km/hr !) => All this is done at `burst-rate’ (+- 100 times per sec). = SPECTACULAR !!

  12. DSP in applications : GSM • GSM Channel Estimation/Compensation • GSM Speech Coding • Original `PCM-signal has 64kbits/sec =8 ksamples/sec*8bits/sample • Aim is to reduce this to <11kbits/sec, while preserving quality! • Coding based on speech generation model (vocal tract,…), where model coefficient are identified for each new speech segment (e.g. 20 msec). • This leads to a least-squares parameter estimation (again), executed +- 50 times per second. Fast algorithm is used, e.g. `Levinson-Durbin’ algorithm. See PART-III on ‘Optimal Filtering’. • Then transmit model coefficients instead of signal samples. • Synthesize speech segment at receiver (should `sounds like’ original speech segment). = SPECTACULAR !!

  13. DSP in applications : GSM • GSM Channel Estimation/Compensation • GSM Speech Coding • GSM Multiple Access Schemes • Capacity increase by time & frequency `multiplexing’ • FDMA : e.g. 125 frequency channels for GSM/900MHz • TDMA : 8 time slots(=users) per channel, `burst mode’ communication (PS: in practice, capacity per cell << 8*125 ! ) See PART-II on ‘Filter Banks & Transmultiplexers’ • Etc.. = BOX FULL OF DSP/MATHEMATICS !! (for only €25)

  14. DSP in applications : Other… • Digital Communications Wireline (xDSL,Powerline), Wireless (GSM, 3G, Wi-Fi, WiMax CDMA, MIMO-transmission,..) • Speech Speech coding (GSM, DECT, ..), Speech synthesis (text-to-speech), Speech recognition • Audio Signal Processing Audio Coding (MP3, AAC, ..), Audio synthesis Editing, Automatic transcription, Dolby/Surround, 3D-audio,. • Image/Video • …

  15. DSP in applications Enabling Technology is • Signal Processing 1G-SP: analog filters 2G-SP: digital filters, FFT’s, etc. 3G-SP: full of mathematics, linear algebra, statistics, etc... • Micro-/Nano-electronics • ... Signals&Systems course (JVDW) DSP-I (PW) DSP-CIS

  16. DSP-II Aims/Scope • Basic signal processing theory/principles filter design, filter banks, optimal filters & adaptive filters …as well as… • Recent/advanced topics robust filter realization, perfect reconstruction filter banks, fast adaptive algorithms, ... • Often `bird’s-eye view’ skip many mathematical details (if possible…  ) selection of topics (non-exhaustive)

  17. Overview (I) • INTRO : Chapter-1 Chapter-2: Signals and Systems Review • Part I : Filter Design & Implementation Chapter-3: IIR & FIR Filter Design Chapter-4: Filter Realization Chapter-5: Filter Implementation

  18. Overview (II) • Part II: Filter Banks & Subband Systems Chapter-6: Filter Banks Intro/Applications (audio coding/CDMA/…) Chapter-7: Filter Banks Theory Chapter-8: DFT-modulated Filter Banks Chapter-9: Special Topics (frequency-domain processing, wavelets…) 3 subband processing 3 G1(z) H1(z) OUT IN 3 subband processing 3 G2(z) H2(z) + 3 subband processing 3 G3(z) H3(z) 3 subband processing 3 G4(z) H4(z)

  19. Overview (III) • Part III : Optimal & Adaptive Filtering Chapter-10: Optimal/Wiener Filters Chapter-11: Adaptive Filters/Recursive Least Squares Chapter-12: Adaptive Filters/LMS Chapter-13: `Fast’ Adaptive Filters Chapter-14: Kalman Filters

  20. Activities : Lectures Lectures: 14 * 2 hrs PS: Time budget = (14*2hrs)*4 = 112 hrs Course Material: • Part I-II-III:Slides (use version 2011-2012 !!) ...download from DSP-CIS webpage • Part III: `Introduction to Adaptive Signal Processing‘ (Marc Moonen & Ian.K. Proudler) = support material, not mandatory ! …(if needed) download from DSP-CIS webpage

  21. Literature / Campus Library Arenberg • A. Oppenheim & R. Schafer `Digital Signal Processing’(Prentice Hall 1977) • L. Jackson `Digital Filters and Signal Processing’(Kluwer 1986) • P.P. Vaidyanathan `Multirate Systems and Filter Banks’(Prentice Hall 1993) • Simon Haykin `Adaptive Filter Theory’(Prentice Hall 1996) • M. Bellanger `Digital Processing of Signals’(Kluwer 1986) • etc... Part-I Part-II Part-III

  22. Literature / DSP-CIS Library • Collection of books is available to support course material • List/reservation via DSP-CIS webpage • Contact: beier.li@esat

  23. Activities : Exercise Sessions `Acoustic Modem Project’ • Digital communication over an acoustic channel (from loudspeaker to microphone) • FFT/IFFT-based modulation format : OFDM (as in ADSL/VDSL, WiFi, …) • Channel estimation, equalization, etc… Rx Tx A-to-D D-to-A +filtering +amplif. +filtering +… ..more on this in Part-2

  24. Activities : Exercise Sessions `Acoustic Modem Project’ • Runs over 8 weeks • Each week • 1 PC/Matlab session (supervised, 2.5hrs) • 2 ‘Homework’ sesions (unsupervised, 2*2.5hrs) PS: Time budget = 8*(2.5hrs+5hrs) = 60 hrs • ‘Deliverables’ after week 2, 4, 6, 8 • Grading: based on deliverables, evaluated during sessions • TAs:alexander.bertrand@esat(English+Dutch) beier.li@esat(English+Chinese) paschalis.tsiaflakis@esat(English+Greek+Dutch) pepe.gilcacho@esat(English+Spanish) PS: groups of 2

  25. Activities : Exam • Oral exam, with preparation time • Open book • Grading : 5 pts for question-1 5 pts for question-2 5 pts for question-3 ps: 1 question (could be) referring to Acoustic Modem Project 5 pts for Acoustic Modem Project evaluation ___ = 20 pts

  26. homes.esat.kuleuven.be/~pepe/dsp-cis/2011-2012 • Contact: pepe.gilcacho@esat • Slides • Project info/schedule • Exams • DSP-CIS Library • FAQs (send questions to pepe.gilcacho@esat or marc.moonen@esat )

  27. Chapter-1 : Introduction • Part-1: • Aims/Scope Why study DSP ? DSP in applications : GSM example • Overview • Activities Lectures: Course Notes/Literature Exercise Sessions: Acoustic Modem Project Exam • Part-2: • Acoustic Modem Project

  28. Discrete-time receiver signal (sampling rate Fs, e.g. 10kHz) Discrete-time transmit signal (sampling rate Fs, e.g. 10kHz) Rx Tx A-to-D D-to-A +filtering +amplif. +filtering +… Introduction / Acoustic Modem • Will consider digital communications over acoustic channel:

  29. Introduction / Acoustic Modem • Will consider digital communications over acoustic channel: Discrete-time receiver signal (sampling rate Fs, e.g. 10kHz) Discrete-time transmit signal (sampling rate Fs, e.g. 10kHz) Rx Tx A-to-D D-to-A +filtering +amplif. +filtering +… This will be the easy part…

  30. Introduction / Acoustic Modem • Will consider digital communications over acoustic channel: …straightforwardly realized (in Matlab/Simulink with `Real-Time Workshop’, see below) Discrete-time receiver signal (sampling rate Fs, e.g. 10kHz) Discrete-time transmit signal (sampling rate Fs, e.g. 10kHz) Rx Tx A-to-D D-to-A +filtering +amplif. +filtering +… means we do not have to deal with hardware issues, RF components, etc.

  31. Introduction / Acoustic Modem • Will consider digital communications over acoustic channel: …and will be modeled by a linear discrete-time transfer function (see below) Discrete-time receiver signal (sampling rate Fs, e.g. 10kHz) Discrete-time transmit signal (sampling rate Fs, e.g. 10kHz) H(z) Rx Tx A-to-D D-to-A +filtering +amplif. +filtering +…

  32. Introduction / Acoustic Modem • Will consider digital communications over acoustic channel: Discrete-time receiver signal (sampling rate Fs, e.g. 10kHz) Discrete-time transmit signal (sampling rate Fs, e.g. 10kHz) Rx Tx A-to-D D-to-A +filtering +amplif. +filtering +… This is the interesting part… (where we will spend most of the time)

  33. Introduction / Acoustic Modem • Will use OFDM as a modulation format • OFDM/DMT is used in ADSL/VDSL, WiFi, DAB, DVB … • OFDM heavily relies on DSP functionalities (FFT/IFFT, …) Orthogonal frequency-division multiplexing From Wikipedia, the free encyclopedia Orthogonal frequency-division multiplexing (OFDM), essentially identical to (…) discrete multi-tone modulation (DMT), is a frequency-division multiplexing (FDM) scheme used as a digital multi-carrier modulation method. A large number of closely-spaced orthogonal sub-carriers are used to carry data. The data is divided into several parallel data streams or channels, one for each sub-carrier. Each sub-carrier is modulated with a conventional modulation scheme (such as quadrature amplitude modulation or phase-shift keying) at a low symbol rate, maintaining total data rates similar to conventional single-carrier modulation schemes in the same bandwidth. OFDM has developed into a popular scheme for wideband digital communication, whether wireless or over copper wires, used in applications such as digital television and audio broadcasting, wireless networking and broadband internet access.

  34. Rx Tx A-to-D D-to-A Introduction / Acoustic Modem Target: Design efficient OFDM based modem (Tx/Rx) for transmission over acoustic channel Specifications: Data rate (e.g. 1kbits/sec), bit error rate (e.g. 0.5%), channel tracking speed, synchronisation, …

  35. Work Plan 8 Weeks: • Week 1: Introduction Matlab/Simulink • Week 2: Acoustic channel measurement & modeling *deliverable* • Week 3-4: OFDM transmitter/receiver design *deliverable* • Week 5-6: OFDM over acoustic channel *deliverable* • Week 7-8: OFDM with adaptive equalization *deliverable*

  36. Week 1 / Matlab & Simulink Introduction Matlab/Simulink

  37. Week 1 / Matlab & Simulink Will also provide basic Simulink scheme… (`Real-Time Workshop’)

  38. Discrete-time receiver signal (sampling rate Fs, e.g. 10kHz) Discrete-time transmit signal (sampling rate Fs, e.g. 10kHz) Rx Tx A-to-D D-to-A +filtering +amplif. +filtering +… Week 2 / Channel Modeling & Evaluation Transmission channel consist of • Tx `front end’: filtering/amplification/Digital-to-Analog conv. • Loudspeaker (ps: cheap loudspeakers mostly have a non-linear characteristic ) • Acoustic channel • Microphone • Rx `front end’: filtering/Analog-to-Digital conv.

  39. Week 2/ Channel Modeling & Evaluation Acoustic channel (`room acoustics’): Acoustic path between loudspeaker and microphone is represented by theacoustic impulse response(which can be recorded/measured) • Observe that : • first there is a dead time • then come the direct path impulse • and some early reflections, which • depend on the geometry of the room • finally there is an exponentially decaying tail called reverberation, corresponding to multiple reflections on walls, objects,...

  40. Week 2 / Channel Modeling & Evaluation Complete transmission channel will be modeled by a discrete-time (FIR `finite impulse response’) transfer function • Pragmatic & good-enough approximation • Model order L depends on sampling rate (e.g. L=100…1000…) H(z) Rx Tx A-to-D D-to-A +filtering +amplif. +filtering +…

  41. Week 2 / Channel Modeling & Evaluation When a discrete-time (Tx) signal x_k is sent over a channel… ..then channel output signal (=Rx input signal) y_k is =`convolution’

  42. Week 2 / Channel Modeling & Evaluation Can now run parameter estimation experiment: • Transmit `well-chosen’ signal x_k • Record corresponding signal y_k y_k H(z) x_k Rx Tx A-to-D D-to-A +filtering +amplif. +filtering +…

  43. Week 2 / Channel Modeling & Evaluation 3. Least squares estimation (i.e. one line of Matlab code ) Carl Friedrich Gauss (1777 – 1855)

  44. Week 2 / Channel Modeling & Evaluation Estimated transmission channel can then be analysed… • Frequency response • Information theoretic capacity ps: noise spectrum? Claude Shannon 1916-2001

  45. Week 3-4 / OFDM modulation DMT – Discrete Multitone Modulation OFDM – Orthogonal Frequency Division Multiplexing Basic idea is to (QAM-)modulate (many) different carriers with low-rate bit streams. The modulated carriers are summed and then transmitted. A high-rate bit stream is thus carried by dividing it into hundreds of low-rate streams. Modulation/demodulation is performed by FFT/IFFT (see below) Now 10 pages of (simple) maths/theory…

  46. Week 3-4 / OFDM modulation 1/10 Consider modulation of a segment of a complex exponential carrier (frequency w) by a `symbol’ X_w, generating a segment of a (time-domain) signal x_k : PS: remember that modulation of sines and cosines is similar/related to modulation of complex exponentials

  47. Week 3-4 / OFDM modulation 2/10 Modulation of N such carriers at once (with well-chosen frequencies, i.e. such that segment always has integer number of periods) is realized by means of N-point (so-called) `Inverse Discrete Fourier Transform’ (IDFT)

  48. Week 3-4 / OFDM modulation 3/10 • PS:Note that modulates a DC signal • PS: To ensure time-domain signal is real-valued, have to choose • PS: The IDFT matrix is a cool matrix: • For any dimension N, an IDFT matrix can be constructed as given on the previous slide. • Its inverse is the so-called DFT matrix (symbol `F’). DFT and IDFT matrices are unitary (up to a scalar), i.e. • The structure of the IDFT matrix allows for a cheap (complexity N.logN instead of N.N) algorithm to compute the matrix-vector product on the previous slide (IFFT `inverse fast Fourier transform’).

  49. Week 3-4 / OFDM modulation 4/10 • So this will be the basic modulation operation at the Tx : • The X_w’s will be (QAM-symbols) defined by the input bit stream • The time-domain signal segments are transmitted over the channel, one after the other. At the receiver, demodulation will be done with an inverse operation (see below: vector of received samples is multiplied by F, based on FFT `fast Fourier transform ). Imag(Xi) Real(Xi)

  50. Week 3-4 / OFDM modulation 5/10 • Sounds simple, but forgot one thing: channel H(z) !! OFDM has an ingenious way of dealing with the channel dispersion, namely through the insertion of a so-called `cyclic prefix’ at the Tx : instead of transmitting N samples, N+L sampes are transmitted (assuming L<<N) : L N

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