Multicarrier transmission DMT/OFDM
|
|
- Owen O’Neal’
- 5 years ago
- Views:
Transcription
1 W. Henkel, International University Bremen 1 Multicarrier transmission DMT/OFDM DMT: Discrete Multitone (wireline, baseband) OFDM: Orthogonal Frequency Division Multiplex (wireless, with carrier, passband) Idea: Sub-division of the frequency band in N narrow sub-bands PSD frequency multiplex Consequences: Symbol duration is N-times longer All reflection components (wireless) received within a single symbol = equalization inside a symbol Problems with narrow-band interference = Requires coding across carriers (frequencies) carrier no.
2 W. Henkel, International University Bremen 2 OFDM: u(t) = 2 R { } N 1 F i e j2π T t i e jω ct i=0, u k = 2 R { } N 1 F i e j 2π N ik e jω ckt /N i=0 DMT: u(t) = 2 N 1 i=0 F ie j2π t T i = 2 N/2 1 i=1 F i e j2π t T i + F i e j2π t T i u k = 2 N 1 i=0 F ie j 2π N ik = 2 N/2 1 i=1 F i e j 2π for F 0 = F N/2 = 0; F i = F N i, i = 1,...,N/2 1 N ik + F i e j 2π N ik m 1 bit Daten seriell parallel Wandler 1 m 2 bit 2 m N bit Modulator + Signal N
3 W. Henkel, International University Bremen 3 The Discrete Fourier Transform (DFT) f i = 1 N 1 N k=0 F k = 1 N 1 N i=0 F k e j 2π N ik = 1 N F ( x = (e j 2π N ) i) i = 0,1,...,N 1 f k e j 2π N ik = 1 ( f x = (e j 2π N ) k) N k = 0,1,...,N 1 IDFT DFT
4 W. Henkel, International University Bremen 4 Some properties: Original domain DFT domain c j = a j b j C(x) = 1 N A(x) B(x) c(x) = 1 N a(x) b(x) c j = 1 N N 1 l=0 a l b j l C k = 1 N N 1 l=0 A l B k l A k B k c j mod N = a j l mod N C k = A k ( ) e j 2π k l N c(x) = a(x) x l mod (x N 1) ( ) c j = a j e j 2π j l N C k mod N = A k l mod N, C(x) = A(x) x l mod (x N 1)
5 W. Henkel, International University Bremen 5 information bitstream serial m 1 m 2 modulation c 1 c 2 IDFT parallel transmitted signal parallel m N c N serial In case of DMT: 1 DFT domain M-QAM conjugates time domain
6 W. Henkel, International University Bremen 6 h(k) a) k x(k) y(k) * h(k) = k k b) x p (k) y p (k) * h(k) = k k c) x z (k) y z (k) * h(k) = k k
7 W. Henkel, International University Bremen 7 Receiver structure of DMT (OFDM) AGC 1AGC2 AGC 3AGC4 Time-domain equalizer (transversal) S P DFT AGC N/2-1 Decoder
8 W. Henkel, International University Bremen 8 Some considerations regarding the power density spectrum without Guard interval: with Guard interval: 1 PSD 1 PSD 1.E-1 1.E-1 1.E-2 1.E-2 1.E carrier no. Spectral influence of the D/A conversion (sample & hold): 1 1.E-1 1.E-2 PSD 1.E carrier no. 512 corresponds to the sampling frequency of ADSL 1.E carrier no.
9 W. Henkel, International University Bremen 9 Criteria for the parameter choices N as big as possible and G as short as possible, in order to minimize the rate loss G/N G roughly as long as the impulse response (delay spread) - otherwise, e.g., time-domain equalization (TEQ) Latency (N) will be limited. N limited by coherence time. Complexity increases with N log N (FFT) Error correcting codes almost always required!
10 W. Henkel, International University Bremen 10 Hughes-Hartogs algorithm Bit allocation 1. Determine the noise powers at all carrier positions 2. Determine the required transmit powers S i,r of carrier i to transmit a data rate of R b/symbol. R = 0,...,R max 3. Determine the incremental transmit powers S i,r = S i,r S i,r 1 WHILE (R < R tot ) { Matrix S i,r, i = 1..N carr, R = 0..R max 4. Search for the carrier i with the smallest incremental power S i,r 5. Increment the bit content of carrier i 6. Delete S i,r from the matrix of power increments } The algorithm delivers bit and power allocation.
11 W. Henkel, International University Bremen 11 Water filling for parallel AWGN channels Theorem: Let us consider n parallel time-discrete, memoryless Gaussian channels with statistically independent noise signals with variances N i, i = 1,2,...,n. Let the total power of the input signals be limited to E, i.e., The channel capacity will be C = n E{Xi 2 } E i=1 n i=1 1 2 log(1 + S i/n i ) and will be achieved, when all input signals are independent and zero-mean Gaussian distributed random variables with variances S i, i = 1,2,...,n, fulfilling the following conditions
12 W. Henkel, International University Bremen 12 choosing B such that n i=1 S i = E S i + N i = B, if N i < B S i = 0, if N i B, B S 1 N 1 S 2 S 3 S 5 N 4 S 6 S 7 N 6 N 3 N 5 N 2 N 7
13 W. Henkel, International University Bremen 13 Proof: I(X; Y) I(X; Y) n i=1 n i=1 I(X i ;Y i ) 1 2 log(1 + S i/n i ) Maximization under side-conditions by applying the Lagrange Multiplier Side condition: n i=1 S i = E Lagrange multiplier: S j [ n i=1 1 2 log(1 + S i/n i ) + λ = S j n i=1 ( E )] n S i = 0, i = 0,1,2,...,n i=1 1 2 log(1 + S i/n i ) = λ 1 2(S j + N j ) = λ = S j + N j = 1 2λ =: B
14 W. Henkel, International University Bremen 14 Algorithm by Chow et al. 1. Determine the SNRs P i /N i of all carrier positions i at constant transmit power density P i = P T /N carr 2. Initialize the margin γ with γ = γ 0 (e.g., 0.1), the iteration counter n iter to zero, and the number of actually used carriers N used to N carr. 3. Computation of the bit allocation and rounding ( R i = log P ) i N i γ ˆR i = R i + 0,5 R i = R i ˆR i Let N 0 be the number of carriers with ˆR i = 0. The number of actually used carriers becomes N used = N carr N 0 4. R Σ = i ˆR i. IF (R Σ = 0) THEN BAD CHANNEL, EXIT
15 W. Henkel, International University Bremen Update of γ: 6. Increment of iteration counter γ := γ 2 R Σ R T N used n iter := n iter + 1; 7. IF (R Σ R T ) (n iter < n max ) Reset of N used to N carr and GOTO 3 8. WHILE (R Σ > R T ) { Search for carrier i with minimum R i (only such i with ˆR i > 0) ˆR i := ˆR i 1 } R i := R i + 1 IF ( ˆR i = 0)N used := N used 1
16 W. Henkel, International University Bremen WHILE (R Σ < R T ) { Search for carrier i with maximum R i (only such i with ˆR i < R max ) ˆR i := ˆR i + 1 } R i := R i 1 IF ( ˆR i = 1)N used := N used Calculate all carrier powers P i of all active carriers according to the desired bit-error probability P b. For quadratic QAM-constellations with R i 4, this means P i = 2 N i (2 R i 1) 3 [ erfc 1 ( Pb R i 2 R i 2 ( 2 R i 1) )] Compute P Σ = i: ˆR i >0 P i and modify the power allocation according to P i := P T P Σ P i.
17 W. Henkel, International University Bremen 17 Explanation of Step 5 ( ) R/N used = log SNR γ. 2 R Σ/N used = 1 + SNR γ Σ, 2 R T /N used = 1 + SNR γ T. 2 (R Σ R T )/N used = 1 + SNR γ Σ 1 + SNR γ T γ T γ Σ,
18 W. Henkel, International University Bremen 18 Algorithm by Yaniv George and Ofer Amrani (ISIT 2004) Essential idea: keep the power allocation constant and let the bit-error rate vary along carriers. the average BER is minimized. This is a very relevant algorithm in practice, since spectral masks are defined in standards. No tolerance band of a few db would be needed any more! One can minimize the BER for a given throughput and power, maximize the throughput for a given power and target BER.
19 W. Henkel, International University Bremen 19 The alg. requires the BERs P b (i, j) for different (quanized) SNR values (index j = 1,...,ν) and bit numbers (index i = 1,...,m), i.e., a table in the form m\snrs 1 2 ν 1 P b (1,1) P b (1,2) P b (1,ν) 2 P b (2,1) P b (2,2) P b (2,ν) 3 P b (3,1) P b (3,2) P b (3,ν). m P b (m,1) P b (m,2) P b (m,ν)
20 W. Henkel, International University Bremen 20 For minimizing the average bit-error rate, we use a table of incremental bit-error rates weighted by the actual number of bits b i We obtain a table in the form P(i, j) = b i P b (i, j) b i 1 P b (i 1, j) m\snrs 1 2 ν 1 P(1, 1) P(1, 2) P(1, ν) 2 P(2, 1) P(2, 2) P(2, ν) 3 P(3, 1) P(3, 2) P(3, ν). m P(m, 1) P(m, 2) P(m, ν)
21 W. Henkel, International University Bremen 21 We accumulate smallest-value entries until the desired target rate (target number of bits) is reached. m\snrs 1 2 ν 1 P(1, 1) P(1, 2) P(1, ν) 2 P(2, 1) P(2, 2) P(2, ν) 3 P(3, 1) P(3, 2) P(3, ν). m P(m, 1) P(m, 2) P(m, ν)
22 W. Henkel, International University Bremen 22 Maximum throughput is obtained when accumulating until the acceptable maximum average bit-error rate is reached m\snrs 1 2 ν i init i 1 i 2 i ν b P b init b 1 P b1 b 2 P b2 b ν P bν 1 P(i 1,1) P(i 2,2) P(i ν,ν) 2 P(i 1 + 1,1) P(i 2 + 1,2) P(i ν + 1,ν) 3 P(i 1 + 2,1) P(i 2 + 2,2) P(i ν + 2,ν). (i is a counter, b the corresponding number of bits. Usually, they may be identical. However, keeping them separate is more general and allows for other increments.)
EE6604 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 informationFBMC/OQAM transceivers for 5G mobile communication systems. François Rottenberg
FBMC/OQAM transceivers for 5G mobile communication systems François Rottenberg Modulation Wikipedia definition: Process of varying one or more properties of a periodic waveform, called the carrier signal,
More informationFast Near-Optimal Energy Allocation for Multimedia Loading on Multicarrier Systems
Fast Near-Optimal Energy Allocation for Multimedia Loading on Multicarrier Systems Michael A. Enright and C.-C. Jay Kuo Department of Electrical Engineering and Signal and Image Processing Institute University
More informationa) Find the compact (i.e. smallest) basis set required to ensure sufficient statistics.
Digital Modulation and Coding Tutorial-1 1. Consider the signal set shown below in Fig.1 a) Find the compact (i.e. smallest) basis set required to ensure sufficient statistics. b) What is the minimum Euclidean
More informationWhen does vectored Multiple Access Channels (MAC) optimal power allocation converge to an FDMA solution?
When does vectored Multiple Access Channels MAC optimal power allocation converge to an FDMA solution? Vincent Le Nir, Marc Moonen, Jan Verlinden, Mamoun Guenach Abstract Vectored Multiple Access Channels
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 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 informationLECTURE 18. Lecture outline Gaussian channels: parallel colored noise inter-symbol interference general case: multiple inputs and outputs
LECTURE 18 Last time: White Gaussian noise Bandlimited WGN Additive White Gaussian Noise (AWGN) channel Capacity of AWGN channel Application: DS-CDMA systems Spreading Coding theorem Lecture outline Gaussian
More informationThe analytic treatment of the error probability due to clipping a solved problem?
International Symposium on Information Theory and its Applications, ISITA2004 Parma, Italy, October 0, 2004 The analytic treatment of the error probability due to clipping a solved problem? Werner HENKEL,
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 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 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 informationOn Improving the BER Performance of Rate-Adaptive Block Transceivers, with Applications to DMT
On Improving the BER Performance of Rate-Adaptive Block Transceivers, with Applications to DMT Yanwu Ding, Timothy N. Davidson and K. Max Wong Department of Electrical and Computer Engineering, McMaster
More informationAdvanced 3 G and 4 G Wireless Communication Prof. Aditya K Jagannathan Department of Electrical Engineering Indian Institute of Technology, Kanpur
Advanced 3 G and 4 G Wireless Communication Prof. Aditya K Jagannathan Department of Electrical Engineering Indian Institute of Technology, Kanpur Lecture - 19 Multi-User CDMA Uplink and Asynchronous CDMA
More informationConstellation Shaping for Communication Channels with Quantized Outputs
Constellation Shaping for Communication Channels with Quantized Outputs, Dr. Matthew C. Valenti and Xingyu Xiang Lane Department of Computer Science and Electrical Engineering West Virginia University
More informationLecture 4 Capacity of Wireless Channels
Lecture 4 Capacity of Wireless Channels I-Hsiang Wang ihwang@ntu.edu.tw 3/0, 014 What we have learned So far: looked at specific schemes and techniques Lecture : point-to-point wireless channel - Diversity:
More informationDigital Band-pass Modulation PROF. MICHAEL TSAI 2011/11/10
Digital Band-pass Modulation PROF. MICHAEL TSAI 211/11/1 Band-pass Signal Representation a t g t General form: 2πf c t + φ t g t = a t cos 2πf c t + φ t Envelope Phase Envelope is always non-negative,
More informationConsider a 2-D constellation, suppose that basis signals =cosine and sine. Each constellation symbol corresponds to a vector with two real components
TUTORIAL ON DIGITAL MODULATIONS Part 3: 4-PSK [2--26] Roberto Garello, Politecnico di Torino Free download (for personal use only) at: www.tlc.polito.it/garello Quadrature modulation Consider a 2-D constellation,
More informationMultiple Antennas in Wireless Communications
Multiple Antennas in Wireless Communications Luca Sanguinetti Department of Information Engineering Pisa University luca.sanguinetti@iet.unipi.it April, 2009 Luca Sanguinetti (IET) MIMO April, 2009 1 /
More informationChapter 4: Continuous channel and its capacity
meghdadi@ensil.unilim.fr Reference : Elements of Information Theory by Cover and Thomas Continuous random variable Gaussian multivariate random variable AWGN Band limited channel Parallel channels Flat
More informationSome UEP Concepts in Coding and Physical Transport
Some UEP Concepts in Coding and Physical Transport Werner Henkel, Neele von Deetzen, and Khaled Hassan School of Engineering and Science Jacobs University Bremen D-28759 Bremen, Germany Email: {w.henkel,
More informationLimited Feedback in Wireless Communication Systems
Limited Feedback in Wireless Communication Systems - Summary of An Overview of Limited Feedback in Wireless Communication Systems Gwanmo Ku May 14, 17, and 21, 2013 Outline Transmitter Ant. 1 Channel N
More informationEstimation of the Capacity of Multipath Infrared Channels
Estimation of the Capacity of Multipath Infrared Channels Jeffrey B. Carruthers Department of Electrical and Computer Engineering Boston University jbc@bu.edu Sachin Padma Department of Electrical and
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 informationRevision of Lecture 5
Revision of Lecture 5 Information transferring across channels Channel characteristics and binary symmetric channel Average mutual information Average mutual information tells us what happens to information
More informationAn Adaptive Blind Channel Shortening Algorithm for MCM Systems
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
More informationTurbo Codes for xdsl modems
Turbo Codes for xdsl modems Juan Alberto Torres, Ph. D. VOCAL Technologies, Ltd. (http://www.vocal.com) John James Audubon Parkway Buffalo, NY 14228, USA Phone: +1 716 688 4675 Fax: +1 716 639 0713 Email:
More informationEE4512 Analog and Digital Communications Chapter 4. Chapter 4 Receiver Design
Chapter 4 Receiver Design Chapter 4 Receiver Design Probability of Bit Error Pages 124-149 149 Probability of Bit Error The low pass filtered and sampled PAM signal results in an expression for the probability
More informationMulti-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User MIMO Systems
Multi-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User MIMO Systems Multi-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User
More informationPrinciples of Communications
Principles of Communications Chapter V: Representation and Transmission of Baseband Digital Signal Yongchao Wang Email: ychwang@mail.xidian.edu.cn Xidian University State Key Lab. on ISN November 18, 2012
More informationELEC E7210: Communication Theory. Lecture 10: MIMO systems
ELEC E7210: Communication Theory Lecture 10: MIMO systems Matrix Definitions, Operations, and Properties (1) NxM matrix a rectangular array of elements a A. an 11 1....... a a 1M. NM B D C E ermitian transpose
More informationElectrical Engineering Written PhD Qualifier Exam Spring 2014
Electrical Engineering Written PhD Qualifier Exam Spring 2014 Friday, February 7 th 2014 Please do not write your name on this page or any other page you submit with your work. Instead use the student
More informationPerformance Analysis and Code Optimization of Low Density Parity-Check Codes on Rayleigh Fading Channels
Performance Analysis and Code Optimization of Low Density Parity-Check Codes on Rayleigh Fading Channels Jilei Hou, Paul H. Siegel and Laurence B. Milstein Department of Electrical and Computer Engineering
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 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 informationLecture 2. Capacity of the Gaussian channel
Spring, 207 5237S, Wireless Communications II 2. Lecture 2 Capacity of the Gaussian channel Review on basic concepts in inf. theory ( Cover&Thomas: Elements of Inf. Theory, Tse&Viswanath: Appendix B) AWGN
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 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 informationConstellation Shaping for Communication Channels with Quantized Outputs
Constellation Shaping for Communication Channels with Quantized Outputs Chandana Nannapaneni, Matthew C. Valenti, and Xingyu Xiang Lane Department of Computer Science and Electrical Engineering West Virginia
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 informationPOWER ALLOCATION TRADEOFFS IN MULTICARRIER AUTHENTICATION SYSTEMS
POWER ALLOCATION TRADEOFFS IN MULTICARRIER AUTHENTICATION SYSTEMS P. L. Yu*, J. S. Baras University of Maryland College Par, MD 074 B. M. Sadler Army Research Laboratory Adelphi, MD 0783 Abstract Physical
More informationNew theoretical framework for OFDM/CDMA systems with peak-limited nonlinearities
Science in China Series F: Information Sciences 7 SCIENCE IN CHINA PRESS Springer New theoretical framewor for OFDM/CDMA systems with pea-limited nonlinearities WANG Jian, ZHANG Lin, SHAN XiuMing & REN
More informationProjects in Wireless Communication Lecture 1
Projects in Wireless Communication Lecture 1 Fredrik Tufvesson/Fredrik Rusek Department of Electrical and Information Technology Lund University, Sweden Lund, Sept 2018 Outline Introduction to the course
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 informationSolutions Communications Technology II WS 2010/2011
Solutions Communications Technology II WS 010/011 Yidong Lang, Henning Schepker NW1, Room N350, Tel.: 041/18-6393 E-mail: lang/ schepker@ant.uni-bremen.de Universität Bremen, FB1 Institut für Telekommunikation
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 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 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 informationMULTICARRIER code-division multiple access (MC-
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 51, NO. 2, FEBRUARY 2005 479 Spectral Efficiency of Multicarrier CDMA Antonia M. Tulino, Member, IEEE, Linbo Li, and Sergio Verdú, Fellow, IEEE Abstract We
More informationComputation of Bit-Error Rate of Coherent and Non-Coherent Detection M-Ary PSK With Gray Code in BFWA Systems
Computation of Bit-Error Rate of Coherent and Non-Coherent Detection M-Ary PSK With Gray Code in BFWA Systems Department of Electrical Engineering, College of Engineering, Basrah University Basrah Iraq,
More informationEE6604 Personal & Mobile Communications. Week 13. Multi-antenna Techniques
EE6604 Personal & Mobile Communications Week 13 Multi-antenna Techniques 1 Diversity Methods Diversity combats fading by providing the receiver with multiple uncorrelated replicas of the same information
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 informationA Computationally Efficient Block Transmission Scheme Based on Approximated Cholesky Factors
A Computationally Efficient Block Transmission Scheme Based on Approximated Cholesky Factors C. Vincent Sinn Telecommunications Laboratory University of Sydney, Australia cvsinn@ee.usyd.edu.au Daniel Bielefeld
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 informationHalf-Duplex Gaussian Relay Networks with Interference Processing Relays
Half-Duplex Gaussian Relay Networks with Interference Processing Relays Bama Muthuramalingam Srikrishna Bhashyam Andrew Thangaraj Department of Electrical Engineering Indian Institute of Technology Madras
More informationPerformance Analysis of Spread Spectrum CDMA systems
1 Performance Analysis of Spread Spectrum CDMA systems 16:33:546 Wireless Communication Technologies Spring 5 Instructor: Dr. Narayan Mandayam Summary by Liang Xiao lxiao@winlab.rutgers.edu WINLAB, Department
More informationL interférence dans les réseaux non filaires
L interférence dans les réseaux non filaires Du contrôle de puissance au codage et alignement Jean-Claude Belfiore Télécom ParisTech 7 mars 2013 Séminaire Comelec Parts Part 1 Part 2 Part 3 Part 4 Part
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 informationA LOWER BOUND ON THE PERFORMANCE OF SIMPLIFIED LINEAR PRECODING FOR VECTORED VDSL
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/228664781 A LOWER BOUND ON THE PERFORMANCE OF SIMPLIFIED LINEAR PRECODING FOR VECTORED VDSL
More informationEE303: Communication Systems
EE303: Communication Systems Professor A. Manikas Chair of Communications and Array Processing Imperial College London Introductory Concepts Prof. A. Manikas (Imperial College) EE303: Introductory Concepts
More informationOptimal Intra-Symbol Transmit Windowing for Multicarrier Modulation
Optimal Intra-Symbol Transmit Windowing for Multicarrier Modulation Thomas Magesacher Department of Information Technology, Lund University, Sweden ISCCSP 2006, Marrakech This work has been supported by
More informationPower Spectral Density of Digital Modulation Schemes
Digital Communication, Continuation Course Power Spectral Density of Digital Modulation Schemes Mikael Olofsson Emil Björnson Department of Electrical Engineering ISY) Linköping University, SE-581 83 Linköping,
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 informationUNIVERSITÀ DEGLI STUDI DI PARMA. Adaptive Signal Processing for Power Line Communications
UNIVERSITÀ DEGLI STUDI DI PARMA Dottorato di Ricerca in Tecnologie dell Informazione XXVIII Ciclo Adaptive Signal Processing for Power Line Communications Coordinatore: Chiar.mo Prof. Marco Locatelli Tutor:
More informationRS(544,514) FEC performance
RS(,) FEC performance Pete Anslow, Ciena IEEE GbE & NGOATH Study Group Ad-Hoc, May Introduction The IEEE.bs Task Force has adopted RS(,) FEC with interleaving of FEC symbols from two FEC codewords to give
More informationLecture 4. Capacity of Fading Channels
1 Lecture 4. Capacity of Fading Channels Capacity of AWGN Channels Capacity of Fading Channels Ergodic Capacity Outage Capacity Shannon and Information Theory Claude Elwood Shannon (April 3, 1916 February
More informationBLIND CFO ESTIMATION USING APPROXIMATE CONJUGATE- SYMMETRY PROPERTY IN OFDM/OQAM
BLIND CFO ESTIMATION USING APPROXIMATE CONJUGATE- SYMMETRY PROPERTY IN OFDM/OQAM S.MD.IMRAN (PG Scholar) 1 B.SIVA REDDY (Ph.D) 2 1 Dept of ECE, G Pulla Reddy College of Engineering & Technology, Kurnool,
More informationIntroduction to Wireless & Mobile Systems. Chapter 4. Channel Coding and Error Control Cengage Learning Engineering. All Rights Reserved.
Introduction to Wireless & Mobile Systems Chapter 4 Channel Coding and Error Control 1 Outline Introduction Block Codes Cyclic Codes CRC (Cyclic Redundancy Check) Convolutional Codes Interleaving Information
More informationECE Information theory Final
ECE 776 - Information theory Final Q1 (1 point) We would like to compress a Gaussian source with zero mean and variance 1 We consider two strategies In the first, we quantize with a step size so that the
More informationInfinite 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 informationBandwidth Efficient and Rate-Matched Low-Density Parity-Check Coded Modulation
Bandwidth Efficient and Rate-Matched Low-Density Parity-Check Coded Modulation Georg Böcherer, Patrick Schulte, Fabian Steiner Chair for Communications Engineering patrick.schulte@tum.de April 29, 2015
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 informationSignal Design for Band-Limited Channels
Wireless Information Transmission System Lab. Signal Design for Band-Limited Channels Institute of Communications Engineering National Sun Yat-sen University Introduction We consider the problem of signal
More informationSummary: SER formulation. Binary antipodal constellation. Generic binary constellation. Constellation gain. 2D constellations
TUTORIAL ON DIGITAL MODULATIONS Part 8a: Error probability A [2011-01-07] 07] Roberto Garello, Politecnico di Torino Free download (for personal use only) at: www.tlc.polito.it/garello 1 Part 8a: Error
More information16.36 Communication Systems Engineering
MIT OpenCourseWare http://ocw.mit.edu 16.36 Communication Systems Engineering Spring 2009 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 16.36: Communication
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 informationSIGNAL SPACE CONCEPTS
SIGNAL SPACE CONCEPTS TLT-5406/0 In this section we familiarize ourselves with the representation of discrete-time and continuous-time communication signals using the concepts of vector spaces. These concepts
More informationLecture 9: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1. Overview. Ragnar Thobaben CommTh/EES/KTH
: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1 Rayleigh Wednesday, June 1, 2016 09:15-12:00, SIP 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication
More informationUplink Performance of Wideband Massive MIMO With One-Bit ADCs
Uplink Performance of Wideband Massive MIMO With One-Bit ADCs Christopher Mollén, Junil Choi, Erik G. Larsson and Robert W. Heath Journal Article N.B.: When citing this work, cite the original article.
More informationLecture 6 Channel Coding over Continuous Channels
Lecture 6 Channel Coding over Continuous Channels I-Hsiang Wang Department of Electrical Engineering National Taiwan University ihwang@ntu.edu.tw November 9, 015 1 / 59 I-Hsiang Wang IT Lecture 6 We have
More informationLecture 18: Gaussian Channel
Lecture 18: Gaussian Channel Gaussian channel Gaussian channel capacity Dr. Yao Xie, ECE587, Information Theory, Duke University Mona Lisa in AWGN Mona Lisa Noisy Mona Lisa 100 100 200 200 300 300 400
More informationChapter 7: Channel coding:convolutional codes
Chapter 7: : Convolutional codes University of Limoges meghdadi@ensil.unilim.fr Reference : Digital communications by John Proakis; Wireless communication by Andreas Goldsmith Encoder representation Communication
More informationIEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 49, NO. 10, OCTOBER
TRANSACTIONS ON INFORMATION THEORY, VOL 49, NO 10, OCTOBER 2003 1 Algebraic Properties of Space Time Block Codes in Intersymbol Interference Multiple-Access Channels Suhas N Diggavi, Member,, Naofal Al-Dhahir,
More informationLecture 8: Orthogonal Frequency Division Multiplexing (OFDM)
Communication Systems Lab Spring 2017 ational Taiwan University Lecture 8: Orthogonal Frequency Division Multiplexing (OFDM) Scribe: 謝秉昂 陳心如 Lecture Date: 4/26, 2017 Lecturer: I-Hsiang Wang 1 Outline 1
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 informationBlind MIMO communication based on Subspace Estimation
Blind MIMO communication based on Subspace Estimation T. Dahl, S. Silva, N. Christophersen, D. Gesbert T. Dahl, S. Silva, and N. Christophersen are at the Department of Informatics, University of Oslo,
More informationGraph-based Codes for Quantize-Map-and-Forward Relaying
20 IEEE Information Theory Workshop Graph-based Codes for Quantize-Map-and-Forward Relaying Ayan Sengupta, Siddhartha Brahma, Ayfer Özgür, Christina Fragouli and Suhas Diggavi EPFL, Switzerland, UCLA,
More informationA Systematic Description of Source Significance Information
A Systematic Description of Source Significance Information Norbert Goertz Institute for Digital Communications School of Engineering and Electronics The University of Edinburgh Mayfield Rd., Edinburgh
More informationEE401: Advanced Communication Theory
EE401: Advanced Communication Theory Professor A. Manikas Chair of Communications and Array Processing Imperial College London Introductory Concepts Prof. A. Manikas (Imperial College) EE.401: Introductory
More informationLattice Reduction Aided Precoding for Multiuser MIMO using Seysen s Algorithm
Lattice Reduction Aided Precoding for Multiuser MIMO using Seysen s Algorithm HongSun An Student Member IEEE he Graduate School of I & Incheon Korea ahs3179@gmail.com Manar Mohaisen Student Member IEEE
More informationNOMA: Principles and Recent Results
NOMA: Principles and Recent Results Jinho Choi School of EECS GIST September 2017 (VTC-Fall 2017) 1 / 46 Abstract: Non-orthogonal multiple access (NOMA) becomes a key technology in 5G as it can improve
More informationMODULATION AND CODING FOR QUANTIZED CHANNELS. Xiaoying Shao and Harm S. Cronie
MODULATION AND CODING FOR QUANTIZED CHANNELS Xiaoying Shao and Harm S. Cronie x.shao@ewi.utwente.nl, h.s.cronie@ewi.utwente.nl University of Twente, Faculty of EEMCS, Signals and Systems Group P.O. box
More informationCoherent Turbo Coded MIMO OFDM
Coherent Turbo Coded MIMO OFDM K Vasudevan Associate Professor Telematics Lab Department of EE Indian Institute of Technology Kanpur email: vasu@iitk.ac.in ICWMC Barcelona Spain, 13th 17th Nov 2016 CCISP
More informationApproximate BER for OFDM Systems Impaired by a Gain Mismatch of a TI-ADC Realization
Approximate BER for OFDM Systems Impaired by a Gain Mismatch of a TI-ADC Realization Vo-Trung-Dung Huynh, ele oels, Heidi Steendam Department of Telecommunications annformation Processing, Ghent University
More informationThis examination consists of 10 pages. Please check that you have a complete copy. Time: 2.5 hrs INSTRUCTIONS
THE UNIVERSITY OF BRITISH COLUMBIA Department of Electrical and Computer Engineering EECE 564 Detection and Estimation of Signals in Noise Final Examination 08 December 2009 This examination consists of
More informationTransmit Covariance Matrices for Broadcast Channels under Per-Modem Total Power Constraints and Non-Zero Signal to Noise Ratio Gap
1 Transmit Covariance Matrices for Broadcast Channels under Per-Modem Total Power Constraints and Non-Zero Signal to Noise Ratio Gap Vincent Le Nir, Marc Moonen, Jochen Maes, Mamoun Guenach Abstract Finding
More informationError Correction and Trellis Coding
Advanced Signal Processing Winter Term 2001/2002 Digital Subscriber Lines (xdsl): Broadband Communication over Twisted Wire Pairs Error Correction and Trellis Coding Thomas Brandtner brandt@sbox.tugraz.at
More informationANALYSIS OF A PARTIAL DECORRELATOR IN A MULTI-CELL DS/CDMA SYSTEM
ANAYSIS OF A PARTIA DECORREATOR IN A MUTI-CE DS/CDMA SYSTEM Mohammad Saquib ECE Department, SU Baton Rouge, A 70803-590 e-mail: saquib@winlab.rutgers.edu Roy Yates WINAB, Rutgers University Piscataway
More informationLecture 12. Block Diagram
Lecture 12 Goals Be able to encode using a linear block code Be able to decode a linear block code received over a binary symmetric channel or an additive white Gaussian channel XII-1 Block Diagram Data
More informationCS6956: Wireless and Mobile Networks Lecture Notes: 2/4/2015
CS6956: Wireless and Mobile Networks Lecture Notes: 2/4/2015 [Most of the material for this lecture has been taken from the Wireless Communications & Networks book by Stallings (2 nd edition).] Effective
More information