We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byRyann Dods
Modified over 2 years ago
II. Modulation & Coding
© Tallal Elshabrawy Design Goals of Communication Systems 1.Maximize transmission bit rate 2.Minimize bit error probability 3.Minimize required transmission power 4.Minimize required system bandwidth 5.Minimize system complexity, computational load & system cost 6.Maximize system utilization 2
© Tallal Elshabrawy Some Tradeoffs in M-PSK Modulaion 1Trades off BER and Energy per Bit 2Trades off BER and Normalized Rate in b/s/Hz 3Trades off Normalized Rate in b/s/Hz and Energy per Bit 3 1 2 3 m=4 m=3 m=1, 2
© Tallal Elshabrawy Shannon-Hartley Capacity Theorem C:System Capacity (bits/s) W:Bandwidth of Communication (Hz) S:Signal Power (Watt) N:Noise Power (Watt) 4 System Capacity for communication over of an AWGN Channel is given by:
© Tallal Elshabrawy Shannon-Hartley Capacity Theorem 5 Practical Systems Unattainable Region
© Tallal Elshabrawy Shannon Capacity in terms of E b /N 0 Consider transmission of a symbol over an AWGN channel 6
© Tallal Elshabrawy Shannon Limit 7 Let
© Tallal Elshabrawy Shannon Limit 8 Shannon Limit=-1.6 dB
© Tallal Elshabrawy Shannon Limit No matter how much/how smart you decrease the rate by using channel coding, it is impossible to achieve communications with very low bit error rate if E b /N 0 falls below -1.6 dB
© Tallal Elshabrawy Shannon Limit 10 Shannon Limit=-1.6 dB BPSK Uncoded P b = 10 -5 QPSK Uncoded P b = 10 -5 8 PSK Uncoded P b =10 -5 16 PSK Uncoded P b =10 -5 Room for improvement by channel coding Normalized Channel Capacity b/s/Hz E b /N 0
© Tallal Elshabrawy 1/3 Repetition Code BPSK Is this really purely a gain? No! We have lost one third of the information transmitted rate 11 Coding Gain= 3.2 dB
© Tallal Elshabrawy 012345678910 -6 10 -5 10 -4 10 -3 10 -2 10 E b /N 0 P b BPSK Uncoded 8 PSK 1/3 Repitition Code 1/3 Repetition Code 8 PSK 12 Coding Gain= -0.5 dB When we don’t sacrifice information rate 1/3 repetition codes did not help us
© Tallal Elshabrawy The waveform generator converts binary data to voltage levels (1 V., -1 V.) The channel has an effect of altering the voltage that was transmitted Waveform detection performs a HARD DECISION by mapping received voltage back to binary values based on decision zones Channel Encoder Waveform Generator Waveform Detection Channel Decoder Channel v r x y v = [v 1 v 2 … v i … v n ] e = [e 1 e 2 … e i … e n ] r = [r 1 r 2 … r i … r n ] x = [x 1 x 2 … x i … x n ] y = [y 1 y 2 … y i … y n ] 0 T 0 T +1 V. -1 V. vivi v i =1 v i =0 xixi 0 y i >0 y i <0 r i =1 r i =0 riri + zizi ]-∞, ∞[ yiyi Hard Decision Decoding
© Tallal Elshabrawy The waveform generator converts binary data to voltage levels (1 V., -1 V.) The channel has an effect of altering the voltage that was transmitted The input to the channel decoder is a vector of voltages rather than a vector of binary values Channel Encoder Waveform Generator Channel Decoder Channel v r x v = [v 1 v 2 … v i … v n ] e = [e 1 e 2 … e i … e n ] r = [r 1 r 2 … r i … r n ] x = [x 1 x 2 … x i … x n ] 0 T 0 T +1 V. -1 V. vivi v i =1 v i =0 xixi + zizi ]-∞, ∞[ riri Soft Decision Decoding
© Tallal Elshabrawy Hard Decision -Each received bit is detected individually -If the voltage is greater than 0 detected bit is 1 -If the voltage is smaller than 0 detected bit is 0 -Detection information of neighbor bits within the same codeword is lost Channel Encoder Waveform Generator Waveform Detection Channel Decoder Channel 0 0 0 r y v = [v 1 v 2 … v i … v n ] e = [e 1 e 2 … e i … e n ] r = [r 1 r 2 … r i … r n ] x = [x 1 x 2 … x i … x n ] y = [y 1 y 2 … y i … y n ] 0 -1 -1 -1 0.1 -0.9 0.1 1 0 1 1 Hard Decision: Example 1/3 Repetition Code BPSK
© Tallal Elshabrawy Soft Decision -If the accumulated voltage within the codeword is greater than 0 detected bit is 1 -If the accumulated voltage within the codeword is smaller than 0 detected bit is 0 -Information of neighbor bits within the same codeword contributes to the channel decoding process Channel Encoder Waveform Generator Channel Decoder Channel 0 0 0 r v = [v 1 v 2 … v i … v n ] e = [e 1 e 2 … e i … e n ] r = [r 1 r 2 … r i … r n ] x = [x 1 x 2 … x i … x n ] y = [y 1 y 2 … y i … y n ] 0 -1 -1 -1 0.1 -0.9 0.1 0 Accumulated Voltage = 0.1-0.9+0.1=-0.7<0 Soft Decision: Example 1/3 Repetition Code BPSK
© Tallal Elshabrawy 1/3 Repetition Code BPSK Soft Decision Channel Coding (1/3 Repetition Code) Waveform Representation Channel Soft Decision Decoding r Important Note
© Tallal Elshabrawy BER Performance Soft Decision 1/3 Repetition Code BPSK Select b*=0 if Note that r 0 r 1 and r 2 are independent and identically distributed. In other words Therefore Similarly
© Tallal Elshabrawy Select b*=0 if BER Performance Soft Decision 1/3 Repetition Code BPSK
© Tallal Elshabrawy where BER Performance Soft Decision 1/3 Repetition Code BPSK n is Gaussian distributed with mean 0 and variance 3N 0 /2
© Tallal Elshabrawy Hard Vs Soft Decision: 1/3 Repetition Code BPSK Coding Gain= 4.7 dB
© Tallal Elshabrawy 1/3 Repetition Code 8 PSK Hard Decision 22 Coding Gain= 1.5 dB
© Tallal Elshabrawy Shannon Limit and BER Performance 23 Shannon Limit=-1.6 dB BPSK Uncoded P b = 10 -5 QPSK Uncoded P b = 10 -5 8 PSK Uncoded P b =10 -5 16 PSK Uncoded P b =10 -5 BPSK 1/3 Rep. Code Hard Decision P b = 10 -5 BPSK 1/3 Rep. Code Sodt Decision P b = 10 -5 Normalized Channel Capacity b/s/Hz E b /N 0 1/3 8PSK 1/3 Rep. Code Hard Decision P b = 10 -5 8PSK 1/3 Rep. Code Soft Decision P b = 10 -5
1 Today, we are going to talk about: Shannon limit Comparison of different modulation schemes Trade-off between modulation and coding.
Factors in Digital Modulation
Logarithms Log Review. Logarithms For example Logarithms.
Fundamentals of Digital Communication 2 Digital communication system Low Pass Filter SamplerQuantizer Channel Encoder Line Encoder Pulse Shaping Filters.
I. Previously on IET.
Digital Communications I: Modulation and Coding Course Term Catharina Logothetis Lecture 13.
Combined Linear & Constant Envelope Modulation
1 Analog/Digital Modulation Analog Modulation The input is continuous signal Used in first generation mobile radio systems such as AMPS in USA. Digital.
Channel Coding Part 1: Block Coding
Lab 2 COMMUNICATION TECHNOLOGY II. Capacity of a System The bit rate of a system increases with an increase in the number of signal levels we use to denote.
© Tallal Elshabrawy Trellis Coded Modulation. © Tallal Elshabrawy Trellis Coded Modulation: Introduction Increases the constellation size compared to.
EE 3220: Digital Communication Dr Hassan Yousif1 Dr. Hassan Yousif Ahmed Department of Electrical Engineering College of Engineering at Wadi Aldwasser.
Iterative Equalization and Decoding
Review of Probability Theory. © Tallal Elshabrawy 2 Review of Probability Theory Experiments, Sample Spaces and Events Axioms of Probability Conditional.
Outline Transmitters (Chapters 3 and 4, Source Coding and Modulation) (week 1 and 2) Receivers (Chapter 5) (week 3 and 4) Received Signal Synchronization.
Dept. of EE, NDHU 1 Chapter Four Bandpass Modulation and Demodulation.
1 Binary Signal Detection In MATLAB weve worked some with AWGN noise and the concept of bit error probability. Lets describe how this is measured.
Coherent phase shift keying In coherent phase shift keying different phase modulation schemes will be covered i.e. binary PSK, quadrature phase shift keying.
4.1 Why Modulate? 이번 발표자료는 연구배경 연구복적 제안시스템 시뮬레이션 향후 연구방향으로 구성되어 있습니다.
Coding Theory. 2 Communication System Channel encoder Source encoder Modulator Demodulator Channel Voice Image Data CRC encoder Interleaver Deinterleaver.
Digital Communications I: Modulation and Coding Course Spring Jeffrey N. Denenberg Lecture 3c: Signal Detection in AWGN.
Information Theory & Coding for Digital Communications Prof JA Ritcey EE 417 Source; Anderson Digital Transmission Engineering 2005.
Submission doc.: IEEE /384r1 Chris Heegard, Texas InstrumentsSlide 1 November 2000 Texas Instruments 141 Stony Circle, Suite 130 Santa Rosa California.
Background. Modulation Types FSK Type BUILD ASK Type BUILD.
ECE 4710: Lecture #31 1 System Performance Chapter 7: Performance of Communication Systems Corrupted by Noise Important Practical Considerations:
Introduction of Low Density Parity Check Codes Mong-kai Ku.
Institute for Experimental Mathematics Ellernstrasse Essen - Germany DATA COMMUNICATION 2-dimensional transmission A.J. Han Vinck May 1, 2003.
Modulation and Coding Trade Offs Ramesh Kumar Lama.
Topics discussed in this section:
Submission May, 2000 Doc: IEEE / 086 Steven Gray, Nokia Slide Brief Overview of Information Theory and Channel Coding Steven D. Gray 1.
Block Coded Modulation Tareq Elhabbash, Yousef Yazji, Mahmoud Amassi.
GMSK - Gaussian Minimum Shift Keying
Bandpass Modulation & Demodulation Detection
DIGITAL COMMUNICATION. Introduction In a data communication system, the output of the data source is transmitted from one point to another. The rate of.
1/15 KLKSK Pertemuan III Analog & Digital Data Shannon Theorem xDSL.
3-2008UP-Copyrights reserved1 ITGD4103 Data Communications and Networks Lecture-11:Data encoding techniques week 12- q-2/ 2008 Dr. Anwar Mousa University.
Introduction to Digital Communication
Turbo Codes. 2 A Need for Better Codes Designing a channel code is always a tradeoff between energy efficiency and bandwidth efficiency. Lower rate Codes.
8.15 Noncoherent orthogonal Modulation(1) Noncoherent orthogonal modulation –If two signal is orthogonal and have the same energy during interval T, carrier.
Quadrature Amplitude Modulation Forrest Sedgwick UC Berkeley EECS Dept. EE290F October 2003.
Multilevel Coding and Iterative Multistage Decoding ELEC 599 Project Presentation Mohammad Jaber Borran Rice University April 21, 2000.
Chapter 6 Information Theory
Combined QPSK and MFSK Communication over an AWGN Channel Jennifer Christensen South Dakota School of Mines & Technology Advisor: Dr. Komo.
Digital Data Transmission ECE 457 Spring Information Representation Communication systems convert information into a form suitable for transmission.
林茂昭 教授 台大電機系 個人專長 錯誤更正碼 數位通訊
Institute for Experimental Mathematics Ellernstrasse Essen - Germany DATA COMMUNICATION introduction A.J. Han Vinck May 10, 2003.
Lecture 3-1: Coding and Error Control
1 Helsinki University of Technology,Communications Laboratory, Timo O. Korhonen Data Communication, Lecture6 Digital Baseband Transmission.
Signal Encoding Techniques. Lecture Learning Outcomes Be able to understand, appreciate and differentiate the different signal encoding criteria available.
ECE 4710: Lecture #12 1 Normalized A = 2 Unipolar NRZ Advantages: 1) Easy to generate for TTL (0, +5V) 2) Single supply voltage 3) Best FNBW Disadvantages:
© 2017 SlidePlayer.com Inc. All rights reserved.