An Adaptive Blind Channel Shortening Algorithm for MCM Systems
|
|
- Duane Martin
- 5 years ago
- Views:
Transcription
1 Hacettepe University Department of Electrical and Electronics Engineering An Adaptive Blind Channel Shortening Algorithm for MCM Systems Blind, Adaptive Channel Shortening Equalizer Algorithm which provides Shortened Channel State Information (BACS-SI) Cenk Toker & Gökhan Altın Hacettepe University, Ankara, Turkey 1/
2 Outline Introduction Channel Shortening in the Literature MMSE SAM Blind, Adaptive Channel Shortening Equaliser Algorithm which provides Shortened Channel State Information (BACS-SI) Definition of the Cost Function, Cost Surface Application of the Genetic Algorithms Simulations and Results Conclusions /
3 Introduction Multicarrier Modulation (MCM) can successfully combat InterSymbol Interference (ISI), but requires Cyclic Prefix (CP). v samples N samples CP s y m b o l ( i ) CP s y m b o l ( i+1) copy copy CP prevents ISI/ICI. CP decreases throughput by N/(N+v). 3/
4 MCM(DMT) System TEQ aims at shortening the channel to a fixed length, rather than full equalisation, FEQ cancels the phase rotations caused by the channel. Data bits Serial to Parallel S/P m 0 bit m 1 bit QAM QAM m N/-1 bit QAM X 0 X 1 X N/-1 X * N/-1 X * 1 X * 0 IFFT x 0 x 1 x N-1 Parallel to Serial P/S * Add CP Output bits P/S QAM QAM QAM 1/B 0 X 0 X 1 1/B 1 FEQ x x 1/B N/-1 X N/-1 x Y 0 Y 1 Y N/-1 Y * N/-1 Y * 1 FFT Remove CP TEQ 4/ Y * 0 y 0 y 1 y N-1 S/P DMT Channel + TX-RX Filters noise +
5 Channel Shortening Channel Shortening Methods in the Literature, - MMSE -MSSNR -MBR, etc. requires Channel State Information (CSI) requires Channel Estimation requires Training Sequence (reduces bit rate) SOLUTION Blind Channel Shortening (does not require training) Blind Channel Shortening Methods in the Literature: MERRY (Martin, et al., 0) SAM (Balakrishnan, et al. 03) SLAM (Nawaz, et al. 04), etc. 5/
6 MMSE n x n n r n Equaliser y n e Channel (TEQ) + n + h w - ŷ n Delay Target Impulse Response (TIR) b MMSE aims at minimising the variance of the difference between the signals at the output of - the Equaliser (TEQ) and - the Target Impulse Response (TIR). 6/
7 SAM* x n Channel h n n + r n Equaliser (TEQ) w y n c=h*w (shortened channel) Adaptive Algorithm Main idea: - If the length of the shortened channel is v+1 taps, - then the length of its autocorrelation R cc will also be v+1 taps. don t care suppress suppress -(v+1) v+1 SAM l= v+ 1 * Balakrishnan, et al., Blind, Adaptive Channel Shortening Sum-Squared Autocorrelation Minimization (SAM), IEEE Trans. Signal Process., Dec.003 7/ J = n c 1 R cc ( l)
8 MMSE vs. SAM MMSE Does NOT require communication CSI (i.e. blind equalisation) X CAN provide shortened channel CSI SAM X BACS-SI 8/
9 Blind Equalisation: Delay BACS-SI Blind, Adaptive Channel Shortening Equaliser which provides Shortened Channel State Information (BACS-SI) n(n) Equaliser R x(n) Channel r(n) yy e(n) + (TEQ) + h - w No access to - the channel coefficients h, or - the input bits x(n) is possible. Only access to - the received signal r(n) Target Impulse Response (TIR) b - the statistics of x(n), i.e. E{x(n)}=0, var{x(n)}=1 9/ R yy ^^
10 BACS-SI Equality of the autocorrelations of the outputs of the upper and lower branches means the autocorrelations of the equalised channel (c=h*w), and the TIR, b, (of length v+1) will be equal. don t care in SAM, cared by TIR in BACS suppress suppress -(v+1) v+1 J BACS = n 1 c l= 0 R yy ( l) R yy ˆ ˆ ( l) J SAM = n c 1 l= v+ 1 R yy ( l) 10/
11 BACS-SI Cost surface of BACS-SI is multimodal: J J BACS BACS = = E{( R n 1 c l= 0 yy ( R ( l) R cc yy ˆˆ ( l) + σ ( l)) n 0 R ww } ( l) R bb ( l)) = n 1 c l= 0 ( R cc ( l) R bb ( l)) - h=[ ] T, b=[1 0.5] T - equaliser: w=[w 1 w w 3 ] T θ = φ = arctan arctan ( ) w + w w 1 ( w w ) / /
12 BACS-SI Proposition: Taking the conjugate/reciprocal of any combination of zeros of a sequence w.r.t. the unit circle in the z-plane does NOT alter the autocorrelation of that sequence. a=[ ] b=[ ] 1/
13 BACS-SI All minima are related to each other by simple zero flipping operations, If we know a single minimum, we can directly find all minima. Only half of the minima perform shortening, Each minimum corresponds to a particular system delay, Each minimum results in different system performance (e.g. bit rate) How to find a minimum? Stochastic gradient descent algorithm Blind operation no prior information for initialisation arbitrary initial point. Variable to update: - TEQ coefficients, w - TIR coefficients, b 13/
14 BACS-SI C.Toker 14/ BACS-SI Composite variable: Single step size, µ Update equation of the iterative algorithm: Iterate until a stopping criterion is satisfied, e.g. Max. no. iterations, to find the optimum TEQ, w, and TIR, b, coefficients. = = + ) ( ) ( ) ( ) ( 1 1 n J n J n J n J n n b w f f f f μ = b w f
15 BACS-SI For proper operation of the MCM system, FEQ coefficients needed. We need the Fourier Trans. of the shortened channel, c, for FEQ. No direct access to the shortened channel, c. Substitute TIR, b, instead, In MMSE Shortened CSI and TIR are (ideally) identical In BACS, their autocorrelations are identical, but themselves may not be. 15/
16 Genetic Algorithms How to find matching shortened CSI, c, and TIR, b? We propose an approach based on genetic algorithms. Each minimum of J BACS results in a different (w,b) pair, Each minimum is connected to others through zero flipping operations for the zeros of both w and b. Problem is binary in nature (inside or outside the unit circle). Genetic algorithm parameters: Each relative position is represented by a gene (inside: 0 or outside: 1) Collection of all w and b genes for a particular minimum of J BACS : a chromosome: 16/
17 Fitness function: Genetic Algorithms We can use pilot tones that are already there (for other puposes) For example, ADSL, 64th tone is reserved for timing recovery, If TEQ & FEQ are running properly, FEQ output at 64th tone is R( 64) = 1 To check whether the shortened channel and TIR are the same for a particular solution (chromosome), we can check k K R( k) 1 Initial population, 18 randomly generated chromosomes, Mutation rate 30%, i.e., 30% of all genes change state (1 0, 0 1), Stop algorithm after 750 iterations. 17/
18 Genetic Algorithms magnitude before GA shortened channel IR TIR magnitude autocorrelation samples after GA samples autocorrelations of shortened channel IR and TIR before/after GA shortened channel IR TIR shortened channel IR before GA shortened channel IR after GA TIR before GA TIR after GA samples 18/
19 SIMULATIONS and RESULTS FFT size 51, TEQ length 16, CP length 3, TIR length 33, and channel CSA test loop 1 Fixed Step Size BACS-SI SAM Adaptive Step Size BACS-SI SAM İterations 19/
20 SIMULATIONS and RESULTS Channel Impulse Response Bit Rate Orig. Channel BACS-SI SAM x magnitude bits per second taps 1 BACS MFB 0.5 MSSNR SAM number of iterations 0/
21 CONCLUSIONS Most of the channel shortening equaliser proposals in the literature assume perfect channel state information. This requires channel estimation utilising training sequences which do not convey information, hence reducing throughput. Blind channel equalisation does not require channel estimation, hence training sequences. The blind channel shortening equaliser in the literature (MERRY, SAM, SLAM, etc.) do not directly provide shortened channel impulse response which is vital for the FEQ, hence the proper operation of an MCM system. The proposed BACS-SI algorithm can provide this information without any additional effort. 1/
22 Thank You. /
Infinite Length Results for Channel Shortening Equalizers
Infinite Length Results for Channel Shortening Equalizers Richard K. Martin, C. Richard Johnson, Jr., Ming Ding, and Brian L. Evans Richard K. Martin and C. Richard Johnson, Jr. Cornell University School
More informationRevision of Lecture 4
Revision of Lecture 4 We have discussed all basic components of MODEM Pulse shaping Tx/Rx filter pair Modulator/demodulator Bits map symbols Discussions assume ideal channel, and for dispersive channel
More informationA FREQUENCY-DOMAIN EIGENFILTER APPROACH FOR EQUALIZATION IN DISCRETE MULTITONE SYSTEMS
A FREQUENCY-DOMAIN EIGENFILTER APPROACH FOR EQUALIZATION IN DISCRETE MULTITONE SYSTEMS Bo Wang and Tulay Adala Department of Computer Science and Electrical Engineering University of Maryland, Baltimore
More informationBlind, Adaptive Channel Shortening by Sum-squared Auto-correlation Minimization (SAM)
Blind, Adaptive Channel Shortening by Sum-squared Auto-correlation Minimization (SAM) Jaiganesh Balakrishnan, Richard K Martin, and C Richard Johnson, Jr Jaiganesh Balakrishnan Texas Instruments Dallas,
More informationChannel Shortening for Bit Rate Maximization in DMT Communication Systems
Channel Shortening for Bit Rate Maximization in DMT Communication Systems Karima Ragoubi, Maryline Hélard, Matthieu Crussière To cite this version: Karima Ragoubi, Maryline Hélard, Matthieu Crussière Channel
More informationOptimal and Adaptive Filtering
Optimal and Adaptive Filtering Murat Üney M.Uney@ed.ac.uk Institute for Digital Communications (IDCOM) 26/06/2017 Murat Üney (IDCOM) Optimal and Adaptive Filtering 26/06/2017 1 / 69 Table of Contents 1
More informationBLIND, ADAPTIVE EQUALIZATION FOR MULTICARRIER RECEIVERS
BLIND, ADAPTIVE EQUALIZATION FOR MULTICARRIER RECEIVERS A Dissertation Presented to the Faculty of the Graduate School of Cornell University in Partial Fulfillment of the Requirements for the Degree of
More informationMulticarrier transmission DMT/OFDM
W. Henkel, International University Bremen 1 Multicarrier transmission DMT/OFDM DMT: Discrete Multitone (wireline, baseband) OFDM: Orthogonal Frequency Division Multiplex (wireless, with carrier, passband)
More informationSingle-Carrier Block Transmission With Frequency-Domain Equalisation
ELEC6014 RCNSs: Additional Topic Notes Single-Carrier Block Transmission With Frequency-Domain Equalisation Professor Sheng Chen School of Electronics and Computer Science University of Southampton Southampton
More informationIntroduction to Constrained Estimation
Introduction to Constrained Estimation Graham C. Goodwin September 2004 2.1 Background Constraints are also often present in estimation problems. A classical example of a constrained estimation problem
More informationOptimal Time Domain Equalization Design for Maximizing Data Rate of Discrete Multi-Tone Systems
IEEE TRANSACTIONS ON SIGNAL PROCESSING 1 Optimal Time Domain Equalization Design for Maximizing Data Rate of Discrete Multi-Tone Systems Milo s Milo sević, Student Member, IEEE, Lúcio F C Pessoa, Senior
More informationAdaptiveFilters. GJRE-F Classification : FOR Code:
Global Journal of Researches in Engineering: F Electrical and Electronics Engineering Volume 14 Issue 7 Version 1.0 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals
More informationLinear Optimum Filtering: Statement
Ch2: Wiener Filters Optimal filters for stationary stochastic models are reviewed and derived in this presentation. Contents: Linear optimal filtering Principle of orthogonality Minimum mean squared error
More informationAdaptive Filtering Part II
Adaptive Filtering Part II In previous Lecture we saw that: Setting the gradient of cost function equal to zero, we obtain the optimum values of filter coefficients: (Wiener-Hopf equation) Adaptive Filtering,
More informationMMSE Decision Feedback Equalization of Pulse Position Modulated Signals
SE Decision Feedback Equalization of Pulse Position odulated Signals AG Klein and CR Johnson, Jr School of Electrical and Computer Engineering Cornell University, Ithaca, NY 4853 email: agk5@cornelledu
More informationAn Equalization Technique for 54 Mbps OFDM Systems
610 IEICE TRANS FUNDAMENTALS, VOLE87 A, NO3 MARCH 200 PAPER Special Section on Applications and Implementations of Digital Signal Processing An Equalization Technique for 5 Mbps OFDM Systems Naihua YUAN,
More informationEqualizer Variants for Discrete Multi-Tone (DMT) Modulation
Equalizer Variants for Discrete Multi-Tone (DMT) Modulation Steffen Trautmann Infineon Technologies Austria AG Summer Academy @ Jacobs University, Bremen, 2007-07-03 Outline 1 Introduction to Multi-Carrier
More informationAdaptive Bit-Interleaved Coded OFDM over Time-Varying Channels
Adaptive Bit-Interleaved Coded OFDM over Time-Varying Channels Jin Soo Choi, Chang Kyung Sung, Sung Hyun Moon, and Inkyu Lee School of Electrical Engineering Korea University Seoul, Korea Email:jinsoo@wireless.korea.ac.kr,
More informationVariable Tap-Length Mixed-Tone RLS-based Per-Tone Equalisation with Adaptive Implementation
Variable Tap-length Mixed-tone RLS-based Per-tone Equalisation with Adaptive Implementation 179 Variable Tap-Length Mixed-Tone RLS-based Per-Tone Equalisation with Adaptive Implementation Suchada Sitjongsataporn
More informationEE6604 Personal & Mobile Communications. Week 15. OFDM on AWGN and ISI Channels
EE6604 Personal & Mobile Communications Week 15 OFDM on AWGN and ISI Channels 1 { x k } x 0 x 1 x x x N- 2 N- 1 IDFT X X X X 0 1 N- 2 N- 1 { X n } insert guard { g X n } g X I n { } D/A ~ si ( t) X g X
More informationOn VDSL Performance and Hardware Implications for Single Carrier Modulation Transceivers
On VDSL Performance and ardware Implications for Single Carrier Modulation Transceivers S. AAR, R.ZUKUNFT, T.MAGESACER Institute for Integrated Circuits - BRIDGELAB Munich University of Technology Arcisstr.
More informationBLIND CHIP-RATE EQUALISATION FOR DS-CDMA DOWNLINK RECEIVER
BLIND CHIP-RATE EQUALISATION FOR DS-CDMA DOWNLINK RECEIVER S Weiss, M Hadef, M Konrad School of Electronics & Computer Science University of Southampton Southampton, UK fsweiss,mhadefg@ecssotonacuk M Rupp
More informationBlind Channel Equalization in Impulse Noise
Blind Channel Equalization in Impulse Noise Rubaiyat Yasmin and Tetsuya Shimamura Graduate School of Science and Engineering, Saitama University 255 Shimo-okubo, Sakura-ku, Saitama 338-8570, Japan yasmin@sie.ics.saitama-u.ac.jp
More informationAdaptive Forgetting-factor RLS-based Initialisation Per-tone Equalisation in Discrete Multitone Systems
Adaptive Forgetting-factor RS-based Initialisation er-tone Equalisation in Discrete Multitone Systems 7 Adaptive Forgetting-factor RS-based Initialisation er-tone Equalisation in Discrete Multitone Systems
More informationEE482: Digital Signal Processing Applications
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu EE482: Digital Signal Processing Applications Spring 2014 TTh 14:30-15:45 CBC C222 Lecture 11 Adaptive Filtering 14/03/04 http://www.ee.unlv.edu/~b1morris/ee482/
More informationAdaptive Filter Theory
0 Adaptive Filter heory Sung Ho Cho Hanyang University Seoul, Korea (Office) +8--0-0390 (Mobile) +8-10-541-5178 dragon@hanyang.ac.kr able of Contents 1 Wiener Filters Gradient Search by Steepest Descent
More informationDigital Communications: A Discrete-Time Approach M. Rice. Errata. Page xiii, first paragraph, bare witness should be bear witness
Digital Communications: A Discrete-Time Approach M. Rice Errata Foreword Page xiii, first paragraph, bare witness should be bear witness Page xxi, last paragraph, You know who you. should be You know who
More informationECS455: Chapter 5 OFDM. ECS455: Chapter 5 OFDM. OFDM: Overview. OFDM Applications. Dr.Prapun Suksompong prapun.com/ecs455
ECS455: Chapter 5 OFDM OFDM: Overview Let S = (S 1, S 2,, S ) contains the information symbols. S IFFT FFT Inverse fast Fourier transform Fast Fourier transform 1 Dr.Prapun Suksompong prapun.com/ecs455
More informationAdaptive Blind Equalizer for HF Channels
Adaptive Blind Equalizer for HF Channels Miroshnikova. Department of Radio engineering Moscow echnical University of Communications and Informatic Moscow, Russia miroshnikova@ieee.org Abstract In this
More informationSimilarities of PMD and DMD for 10Gbps Equalization
Similarities of PMD and DMD for 10Gbps Equalization Moe Win Jack Winters win/jhw@research.att.com AT&T Labs-Research (Some viewgraphs and results curtesy of Julien Porrier) Outline Polarization Mode Dispersion
More informationTurbo-per-Tone Equalization for ADSL Systems
EURASIP Journal on Applied Signal Processing 2005:6, 852 860 c 2005 H. Vanhaute and M. Moonen Turbo-per-Tone Equalization for ADSL Systems Hilde Vanhaute ESAT/SCD, Katholieke Universiteit Leuven, Kasteelpark
More informationTurbo per tone equalization for ADSL systems 1
Departement Elektrotechniek ESAT-SISTA/TR 2003-186a Turbo per tone equalization for ADSL systems 1 Hilde Vanhaute and Marc Moonen 2 3 January 2004 Accepted for publication in the Proceedings of the IEEE
More informationDetermining the Optimal Decision Delay Parameter for a Linear Equalizer
International Journal of Automation and Computing 1 (2005) 20-24 Determining the Optimal Decision Delay Parameter for a Linear Equalizer Eng Siong Chng School of Computer Engineering, Nanyang Technological
More informationEE482: Digital Signal Processing Applications
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu EE482: Digital Signal Processing Applications Spring 2014 TTh 14:30-15:45 CBC C222 Lecture 11 Adaptive Filtering 14/03/04 http://www.ee.unlv.edu/~b1morris/ee482/
More informationDecision-Point Signal to Noise Ratio (SNR)
Decision-Point Signal to Noise Ratio (SNR) Receiver Decision ^ SNR E E e y z Matched Filter Bound error signal at input to decision device Performance upper-bound on ISI channels Achieved on memoryless
More informationCyclic Prefix based Enhanced Data Recovery in OFDM
Cyclic Prefix based Enhanced Data Recovery in OFDM T. Y. Al-Naffouri 1 and A. Quadeer 2 Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Saudi Arabia, Email: {naffouri
More informationAdaptive Space-Time Shift Keying Based Multiple-Input Multiple-Output Systems
ACSTSK Adaptive Space-Time Shift Keying Based Multiple-Input Multiple-Output Systems Professor Sheng Chen Electronics and Computer Science University of Southampton Southampton SO7 BJ, UK E-mail: sqc@ecs.soton.ac.uk
More informationEfficient Semi-Blind Channel Estimation and Equalization Based on a Parametric Channel Representation
Efficient Semi-Blind Channel Estimation and Equalization Based on a Parametric Channel Representation Presenter: Kostas Berberidis University of Patras Computer Engineering & Informatics Department Signal
More informationGEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING Final Examination - Fall 2015 EE 4601: Communication Systems
GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING Final Examination - Fall 2015 EE 4601: Communication Systems Aids Allowed: 2 8 1/2 X11 crib sheets, calculator DATE: Tuesday
More informationDesign of MMSE Multiuser Detectors using Random Matrix Techniques
Design of MMSE Multiuser Detectors using Random Matrix Techniques Linbo Li and Antonia M Tulino and Sergio Verdú Department of Electrical Engineering Princeton University Princeton, New Jersey 08544 Email:
More informationInformation Theoretic Imaging
Information Theoretic Imaging WU Faculty: J. A. O Sullivan WU Doctoral Student: Naveen Singla Boeing Engineer: James Meany First Year Focus: Imaging for Data Storage Image Reconstruction Data Retrieval
More informationRADIO SYSTEMS ETIN15. Lecture no: Equalization. Ove Edfors, Department of Electrical and Information Technology
RADIO SYSTEMS ETIN15 Lecture no: 8 Equalization Ove Edfors, Department of Electrical and Information Technology Ove.Edfors@eit.lth.se Contents Inter-symbol interference Linear equalizers Decision-feedback
More informationImproved Channel Estimation Methods based on PN sequence for TDS-OFDM
Improved Channel Estimation Methods based on P sequence for TDS-OFDM Ming Liu, Matthieu Crussière, Jean-François Hélard Université Européenne de Bretagne (UEB) ISA, IETR, UMR 664, F-35708, Rennes, France
More information26. Filtering. ECE 830, Spring 2014
26. Filtering ECE 830, Spring 2014 1 / 26 Wiener Filtering Wiener filtering is the application of LMMSE estimation to recovery of a signal in additive noise under wide sense sationarity assumptions. Problem
More informationEE4601 Communication Systems
EE4601 Communication Systems Week 13 Linear Zero Forcing Equalization 0 c 2012, Georgia Institute of Technology (lect13 1) Equalization The cascade of the transmit filter g(t), channel c(t), receiver filter
More informationOptimized Impulses for Multicarrier Offset-QAM
Optimized Impulses for ulticarrier Offset-QA Stephan Pfletschinger, Joachim Speidel Institut für Nachrichtenübertragung Universität Stuttgart, Pfaffenwaldring 47, D-7469 Stuttgart, Germany Abstract The
More informationLinear and Nonlinear Iterative Multiuser Detection
1 Linear and Nonlinear Iterative Multiuser Detection Alex Grant and Lars Rasmussen University of South Australia October 2011 Outline 1 Introduction 2 System Model 3 Multiuser Detection 4 Interference
More informationOptimal Design of Real and Complex Minimum Phase Digital FIR Filters
Optimal Design of Real and Complex Minimum Phase Digital FIR Filters Niranjan Damera-Venkata and Brian L. Evans Embedded Signal Processing Laboratory Dept. of Electrical and Computer Engineering The University
More informationData-aided and blind synchronization
PHYDYAS Review Meeting 2009-03-02 Data-aided and blind synchronization Mario Tanda Università di Napoli Federico II Dipartimento di Ingegneria Biomedica, Elettronicae delle Telecomunicazioni Via Claudio
More informationAdaptive MMSE Equalizer with Optimum Tap-length and Decision Delay
Adaptive MMSE Equalizer with Optimum Tap-length and Decision Delay Yu Gong, Xia Hong and Khalid F. Abu-Salim School of Systems Engineering The University of Reading, Reading RG6 6AY, UK E-mail: {y.gong,x.hong,k.f.abusalem}@reading.ac.uk
More informationBASICS OF DETECTION AND ESTIMATION THEORY
BASICS OF DETECTION AND ESTIMATION THEORY 83050E/158 In this chapter we discuss how the transmitted symbols are detected optimally from a noisy received signal (observation). Based on these results, optimal
More informationInterleave Division Multiple Access. Li Ping, Department of Electronic Engineering City University of Hong Kong
Interleave Division Multiple Access Li Ping, Department of Electronic Engineering City University of Hong Kong 1 Outline! Introduction! IDMA! Chip-by-chip multiuser detection! Analysis and optimization!
More informationAnalog Electronics 2 ICS905
Analog Electronics 2 ICS905 G. Rodriguez-Guisantes Dépt. COMELEC http://perso.telecom-paristech.fr/ rodrigez/ens/cycle_master/ November 2016 2/ 67 Schedule Radio channel characteristics ; Analysis and
More informationCoherentDetectionof OFDM
Telematics Lab IITK p. 1/50 CoherentDetectionof OFDM Indo-UK Advanced Technology Centre Supported by DST-EPSRC K Vasudevan Associate Professor vasu@iitk.ac.in Telematics Lab Department of EE Indian Institute
More informationDirect-Sequence Spread-Spectrum
Chapter 3 Direct-Sequence Spread-Spectrum In this chapter we consider direct-sequence spread-spectrum systems. Unlike frequency-hopping, a direct-sequence signal occupies the entire bandwidth continuously.
More informationImproved Detected Data Processing for Decision-Directed Tracking of MIMO Channels
Improved Detected Data Processing for Decision-Directed Tracking of MIMO Channels Emna Eitel and Joachim Speidel Institute of Telecommunications, University of Stuttgart, Germany Abstract This paper addresses
More informationA Dual-mode Blind Equalization Algorithm for Improving the Channel Equalized Performance
Journal of Communications Vol. 9, No., May 14 A Dual-mode Blind Equalization Algorithm for Improving the Channel Equalized Performance Jing Zhang, Zhihui Ye, and Qi Feng School of Electronic Science and
More informationV. Adaptive filtering Widrow-Hopf Learning Rule LMS and Adaline
V. Adaptive filtering Widrow-Hopf Learning Rule LMS and Adaline Goals Introduce Wiener-Hopf (WH) equations Introduce application of the steepest descent method to the WH problem Approximation to the Least
More informationMMSE DECISION FEEDBACK EQUALIZER FROM CHANNEL ESTIMATE
MMSE DECISION FEEDBACK EQUALIZER FROM CHANNEL ESTIMATE M. Magarini, A. Spalvieri, Dipartimento di Elettronica e Informazione, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milano (Italy),
More informationOptimal and Adaptive Filtering
Optimal and Adaptive Filtering Murat Üney M.Uney@ed.ac.uk Institute for Digital Communications (IDCOM) 27/06/2016 Murat Üney (IDCOM) Optimal and Adaptive Filtering 27/06/2016 1 / 69 This presentation aims
More informationRECENT advance in digital signal processing technology
Acceleration of Genetic Algorithm for Peak Power Reduction of OFDM Signal Noritaka Shigei, Hiromi Miyajima, Keisuke Ozono, and Kentaro Araki Abstract Orthogonal frequency division multiplexing (OFDM) is
More informationRevision of Lecture 4
Revision of Lecture 4 We have completed studying digital sources from information theory viewpoint We have learnt all fundamental principles for source coding, provided by information theory Practical
More informationIMPROVEMENTS IN ACTIVE NOISE CONTROL OF HELICOPTER NOISE IN A MOCK CABIN ABSTRACT
IMPROVEMENTS IN ACTIVE NOISE CONTROL OF HELICOPTER NOISE IN A MOCK CABIN Jared K. Thomas Brigham Young University Department of Mechanical Engineering ABSTRACT The application of active noise control (ANC)
More informationMassachusetts Institute of Technology
Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.011: Introduction to Communication, Control and Signal Processing QUIZ, April 1, 010 QUESTION BOOKLET Your
More informationAnalysis of Receiver Quantization in Wireless Communication Systems
Analysis of Receiver Quantization in Wireless Communication Systems Theory and Implementation Gareth B. Middleton Committee: Dr. Behnaam Aazhang Dr. Ashutosh Sabharwal Dr. Joseph Cavallaro 18 April 2007
More informationDecision Weighted Adaptive Algorithms with Applications to Wireless Channel Estimation
Decision Weighted Adaptive Algorithms with Applications to Wireless Channel Estimation Shane Martin Haas April 12, 1999 Thesis Defense for the Degree of Master of Science in Electrical Engineering Department
More informationStatistical and Adaptive Signal Processing
r Statistical and Adaptive Signal Processing Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing Dimitris G. Manolakis Massachusetts Institute of Technology Lincoln Laboratory
More informationEstimation of the Optimum Rotational Parameter for the Fractional Fourier Transform Using Domain Decomposition
Estimation of the Optimum Rotational Parameter for the Fractional Fourier Transform Using Domain Decomposition Seema Sud 1 1 The Aerospace Corporation, 4851 Stonecroft Blvd. Chantilly, VA 20151 Abstract
More informationEE 602 TERM PAPER PRESENTATION Richa Tripathi Mounika Boppudi FOURIER SERIES BASED MODEL FOR STATISTICAL SIGNAL PROCESSING - CHONG YUNG CHI
EE 602 TERM PAPER PRESENTATION Richa Tripathi Mounika Boppudi FOURIER SERIES BASED MODEL FOR STATISTICAL SIGNAL PROCESSING - CHONG YUNG CHI ABSTRACT The goal of the paper is to present a parametric Fourier
More informationESE 531: Digital Signal Processing
ESE 531: Digital Signal Processing Lec 22: April 10, 2018 Adaptive Filters Penn ESE 531 Spring 2018 Khanna Lecture Outline! Circular convolution as linear convolution with aliasing! Adaptive Filters Penn
More informationEngineering Part IIB: Module 4F10 Statistical Pattern Processing Lecture 5: Single Layer Perceptrons & Estimating Linear Classifiers
Engineering Part IIB: Module 4F0 Statistical Pattern Processing Lecture 5: Single Layer Perceptrons & Estimating Linear Classifiers Phil Woodland: pcw@eng.cam.ac.uk Michaelmas 202 Engineering Part IIB:
More informationA Canonical Genetic Algorithm for Blind Inversion of Linear Channels
A Canonical Genetic Algorithm for Blind Inversion of Linear Channels Fernando Rojas, Jordi Solé-Casals, Enric Monte-Moreno 3, Carlos G. Puntonet and Alberto Prieto Computer Architecture and Technology
More informationImage Dependent Log-likelihood Ratio Allocation for Repeat Accumulate Code based Decoding in Data Hiding Channels
Image Dependent Log-likelihood Ratio Allocation for Repeat Accumulate Code based Decoding in Data Hiding Channels Anindya Sarkar and B. S. Manjunath Department of Electrical and Computer Engineering, University
More informationSquare Root Raised Cosine Filter
Wireless Information Transmission System Lab. Square Root Raised Cosine Filter Institute of Communications Engineering National Sun Yat-sen University Introduction We consider the problem of signal design
More informationBlind Equalization Based on Direction Gradient Algorithm under Impulse Noise Environment
Blind Equalization Based on Direction Gradient Algorithm under Impulse Noise Environment XIAO YING 1,, YIN FULIANG 1 1. Faculty of Electronic Information and Electrical Engineering Dalian University of
More informationMinimum BER Linear Transceivers for Block. Communication Systems. Lecturer: Tom Luo
Minimum BER Linear Transceivers for Block Communication Systems Lecturer: Tom Luo Outline Block-by-block communication Abstract model Applications Current design techniques Minimum BER precoders for zero-forcing
More informationCSC 4510 Machine Learning
10: Gene(c Algorithms CSC 4510 Machine Learning Dr. Mary Angela Papalaskari Department of CompuBng Sciences Villanova University Course website: www.csc.villanova.edu/~map/4510/ Slides of this presenta(on
More informationLecture 9 Evolutionary Computation: Genetic algorithms
Lecture 9 Evolutionary Computation: Genetic algorithms Introduction, or can evolution be intelligent? Simulation of natural evolution Genetic algorithms Case study: maintenance scheduling with genetic
More informationA low complexity Soft-Input Soft-Output MIMO detector which combines a Sphere Decoder with a Hopfield Network
A low complexity Soft-Input Soft-Output MIMO detector which combines a Sphere Decoder with a Hopfield Network Daniel J. Louw, Philip R. Botha, B.T. Maharaj Department of Electrical, Electronic and Computer
More informationGeometric Semantic Genetic Programming (GSGP): theory-laden design of semantic mutation operators
Geometric Semantic Genetic Programming (GSGP): theory-laden design of semantic mutation operators Andrea Mambrini 1 University of Birmingham, Birmingham UK 6th June 2013 1 / 33 Andrea Mambrini GSGP: theory-laden
More informationGRADIENT DESCENT. CSE 559A: Computer Vision GRADIENT DESCENT GRADIENT DESCENT [0, 1] Pr(y = 1) w T x. 1 f (x; θ) = 1 f (x; θ) = exp( w T x)
0 x x x CSE 559A: Computer Vision For Binary Classification: [0, ] f (x; ) = σ( x) = exp( x) + exp( x) Output is interpreted as probability Pr(y = ) x are the log-odds. Fall 207: -R: :30-pm @ Lopata 0
More informationON COMPENSATING ISI, ICI AND NARROWBAND INTERFERENCE IN GENERALIZED DISCRETE MULTITONE MODULATION
ON COMPENSATING ISI, ICI AND NARROWBAND INTERFERENCE IN GENERALIZED DISCRETE MULTITONE MODULATION Johannes Schwarz, Norbert J. Fliege, and Markus Gaida University of Mannheim, Chair of Electrical Engineering,
More informationIS NEGATIVE STEP SIZE LMS ALGORITHM STABLE OPERATION POSSIBLE?
IS NEGATIVE STEP SIZE LMS ALGORITHM STABLE OPERATION POSSIBLE? Dariusz Bismor Institute of Automatic Control, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland, e-mail: Dariusz.Bismor@polsl.pl
More informationIntelligent Control. Module I- Neural Networks Lecture 7 Adaptive Learning Rate. Laxmidhar Behera
Intelligent Control Module I- Neural Networks Lecture 7 Adaptive Learning Rate Laxmidhar Behera Department of Electrical Engineering Indian Institute of Technology, Kanpur Recurrent Networks p.1/40 Subjects
More informationThe interference-reduced energy loading for multi-code HSDPA systems
Gurcan et al. EURASIP Journal on Wireless Communications and Networing 2012, 2012:127 RESEARC Open Access The interference-reduced energy loading for multi-code SDPA systems Mustafa K Gurcan *, Irina Ma
More informationCEPSTRAL ANALYSIS SYNTHESIS ON THE MEL FREQUENCY SCALE, AND AN ADAPTATIVE ALGORITHM FOR IT.
CEPSTRAL ANALYSIS SYNTHESIS ON THE EL FREQUENCY SCALE, AND AN ADAPTATIVE ALGORITH FOR IT. Summarized overview of the IEEE-publicated papers Cepstral analysis synthesis on the mel frequency scale by Satochi
More informationCommunications and Signal Processing Spring 2017 MSE Exam
Communications and Signal Processing Spring 2017 MSE Exam Please obtain your Test ID from the following table. You must write your Test ID and name on each of the pages of this exam. A page with missing
More informationELEC E7210: Communication Theory. Lecture 4: Equalization
ELEC E7210: Communication Theory Lecture 4: Equalization Equalization Delay sprea ISI irreucible error floor if the symbol time is on the same orer as the rms elay sprea. DF: Equalization a receiver signal
More informationMaximum Achievable Diversity for MIMO-OFDM Systems with Arbitrary. Spatial Correlation
Maximum Achievable Diversity for MIMO-OFDM Systems with Arbitrary Spatial Correlation Ahmed K Sadek, Weifeng Su, and K J Ray Liu Department of Electrical and Computer Engineering, and Institute for Systems
More informationChannel Estimation with Low-Precision Analog-to-Digital Conversion
Channel Estimation with Low-Precision Analog-to-Digital Conversion Onkar Dabeer School of Technology and Computer Science Tata Institute of Fundamental Research Mumbai India Email: onkar@tcs.tifr.res.in
More informationCarrier frequency estimation. ELEC-E5410 Signal processing for communications
Carrier frequency estimation ELEC-E54 Signal processing for communications Contents. Basic system assumptions. Data-aided DA: Maximum-lielihood ML estimation of carrier frequency 3. Data-aided: Practical
More informationIterative Timing Recovery
Iterative Timing Recovery John R. Barry School of Electrical and Computer Engineering, Georgia Tech Atlanta, Georgia U.S.A. barry@ece.gatech.edu 0 Outline Timing Recovery Tutorial Problem statement TED:
More informationDesign of Sparse Filters for Channel Shortening
Journal of Signal Processing Systems manuscript No. (will be inserted by the editor) Design of Sparse Filters for Channel Shortening Aditya Chopra Brian L. Evans Received: / Accepted: Abstract Channel
More informationEE5713 : Advanced Digital Communications
EE5713 : Advanced Digital Communications Week 12, 13: Inter Symbol Interference (ISI) Nyquist Criteria for ISI Pulse Shaping and Raised-Cosine Filter Eye Pattern Equalization (On Board) 20-May-15 Muhammad
More informationImage Compression using DPCM with LMS Algorithm
Image Compression using DPCM with LMS Algorithm Reenu Sharma, Abhay Khedkar SRCEM, Banmore -----------------------------------------------------------------****---------------------------------------------------------------
More informationChapter 10. Timing Recovery. March 12, 2008
Chapter 10 Timing Recovery March 12, 2008 b[n] coder bit/ symbol transmit filter, pt(t) Modulator Channel, c(t) noise interference form other users LNA/ AGC Demodulator receive/matched filter, p R(t) sampler
More informationSpectrally Concentrated Impulses for Digital Multicarrier Modulation
Spectrally Concentrated Impulses for Digital ulticarrier odulation Dipl.-Ing. Stephan Pfletschinger and Prof. Dr.-Ing. Joachim Speidel Institut für Nachrichtenübertragung, Universität Stuttgart, Pfaffenwaldring
More informationA SIMULATION AND GRAPH THEORETICAL ANALYSIS OF CERTAIN PROPERTIES OF SPECTRAL NULL CODEBOOKS
6 SOUTH AFRICAN INSTITUTE OF ELECTRICAL ENGINEERS Vol.() September A SIMULATION AND GRAPH THEORETICAL ANALYSIS OF CERTAIN PROPERTIES OF SPECTRAL NULL CODEBOOKS K. Ouahada and H. C. Ferreira Department
More informationBlind Source Separation with a Time-Varying Mixing Matrix
Blind Source Separation with a Time-Varying Mixing Matrix Marcus R DeYoung and Brian L Evans Department of Electrical and Computer Engineering The University of Texas at Austin 1 University Station, Austin,
More information