1 Today, we are going to talk about: Shannon limit Comparison of different modulation schemes Trade-off between modulation and coding.

Slides:



Advertisements
Similar presentations
Signal Encoding Techniques
Advertisements

II. Modulation & Coding. © Tallal Elshabrawy Design Goals of Communication Systems 1.Maximize transmission bit rate 2.Minimize bit error probability 3.Minimize.
EE359 – Lecture 8 Outline Capacity of Fading channels Fading Known at TX and RX Optimal Rate and Power Adaptation Channel Inversion with Fixed Rate Capacity.
1 Helsinki University of Technology,Communications Laboratory, Timo O. Korhonen Data Communication, Lecture6 Digital Baseband Transmission.
Information Theory EE322 Al-Sanie.
Capacity of Wireless Channels
Combined QPSK and MFSK Communication over an AWGN Channel Jennifer Christensen South Dakota School of Mines & Technology Advisor: Dr. Komo.
1/15 KLKSK Pertemuan III Analog & Digital Data Shannon Theorem xDSL.
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.
Cellular Communications
Digital Data Transmission ECE 457 Spring Information Representation Communication systems convert information into a form suitable for transmission.
Quadrature Amplitude Modulation Forrest Sedgwick UC Berkeley EECS Dept. EE290F October 2003.
Digital Communications I: Modulation and Coding Course Term 3 – 2008 Catharina Logothetis Lecture 2.
Coded Modulation in Fading Channels Ryan Aures Matthew Holland ECE 492 Mobile Communications.
Digital communications I: Modulation and Coding Course Period Catharina Logothetis Lecture 6.
Digital Communications I: Modulation and Coding Course Term Catharina Logothetis Lecture 13.
Space Time Block Codes Poornima Nookala.
EE 3220: Digital Communication Dr Hassan Yousif 1 Dr. Hassan Yousif Ahmed Department of Electrical Engineering College of Engineering at Wadi Aldwasser.
Digital Communications I: Modulation and Coding Course Spring Jeffrey N. Denenberg Lecture 4: BandPass Modulation/Demodulation.
Digital Communications I: Modulation and Coding Course
DSP for Software Radio Waveform Processing – Single Carrier Systems Dr. Jamil Ahmad.
EE 6332, Spring, 2014 Wireless Communication Zhu Han Department of Electrical and Computer Engineering Class 12 Feb. 24 nd, 2014.
4.1 Why Modulate? 이번 발표자료는 연구배경 연구복적 제안시스템 시뮬레이션 향후 연구방향으로 구성되어 있습니다.
Formatting and Baseband Modulation
ECE 4371, Fall, 2014 Introduction to Telecommunication Engineering/Telecommunication Laboratory Zhu Han Department of Electrical and Computer Engineering.
Lecture 3-1: Coding and Error Control
Multilevel Coding and Iterative Multistage Decoding ELEC 599 Project Presentation Mohammad Jaber Borran Rice University April 21, 2000.
EE 3220: Digital Communication Dr Hassan Yousif1 Dr. Hassan Yousif Ahmed Department of Electrical Engineering College of Engineering at Wadi Aldwasser.
Digital Communication I: Modulation and Coding Course
Pass-band Data Transmission
Fundamentals of Digital Communication 2 Digital communication system Low Pass Filter SamplerQuantizer Channel Encoder Line Encoder Pulse Shaping Filters.
CHAPTER 6 PASS-BAND DATA TRANSMISSION
Lecture 71 Today, we are going to talk about: Some bandpass modulation schemes used in DCS for transmitting information over channel M-PAM, M-PSK, M-FSK,
Wireless Mobile Communication and Transmission Lab. Chapter 8 Application of Error Control Coding.
Rohit Iyer Seshadri and Matthew C. Valenti
1 Analog/Digital Modulation Analog Modulation The input is continuous signal Used in first generation mobile radio systems such as AMPS in USA. Digital.
1 Information in Continuous Signals f(t) t 0 In practice, many signals are essentially analogue i.e. continuous. e.g. speech signal from microphone, radio.
GMSK - Gaussian Minimum Shift Keying
Factors in Digital Modulation
Digital Communications I: Modulation and Coding Course Term Catharina Logothetis Lecture 12.
Introduction of Low Density Parity Check Codes Mong-kai Ku.
Coding Theory. 2 Communication System Channel encoder Source encoder Modulator Demodulator Channel Voice Image Data CRC encoder Interleaver Deinterleaver.
DIGITAL COMMUNICATIONS Linear Block Codes
TI Cellular Mobile Communication Systems Lecture 4 Engr. Shahryar Saleem Assistant Professor Department of Telecom Engineering University of Engineering.
EE359 – Lecture 13 Outline Adaptive MQAM: optimal power and rate Finite Constellation Sets Practical Constraints Update rate Estimation error Estimation.
OQPSK & p/4 DQPSK Offset Quadrature Phase Shift Keying  OQPSK
Dept. of EE, NDHU 1 Chapter Four Bandpass Modulation and Demodulation.
Digital Communications Chapeter 3. Baseband Demodulation/Detection Signal Processing Lab.
1 Coded modulation So far: Binary coding Binary modulation Will send R bits/symbol (spectral efficiency = R) Constant transmission rate: Requires bandwidth.
Introduction to Digital Communication
When a signal is transmitted over a channel, the frequency band and bandwidth of the channel must match the signal frequency characteristics. Usually,
Combined Linear & Constant Envelope Modulation
Chapter : Digital Modulation 4.2 : Digital Transmission
Constellation Diagram
Bandpass Modulation & Demodulation Detection
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.
Digital Modulation Basics
8.15 Noncoherent orthogonal Modulation(1) Noncoherent orthogonal modulation –If two signal is orthogonal and have the same energy during interval T, carrier.
CHAPTER 4. OUTLINES 1. Digital Modulation Introduction Information capacity, Bits, Bit Rate, Baud, M- ary encoding ASK, FSK, PSK, QPSK, QAM 2. Digital.
Modulation and Coding Trade Offs Ramesh Kumar Lama.
UNIT-IV PASSBAND TRANSMISSION MODEL
Principios de Comunicaciones EL4005
Space Time Codes.
CSE 5345 – Fundamentals of Wireless Networks
Rohit Iyer Seshadri and Matthew C. Valenti
TLEN 5830-AWL Advanced Wireless Lab
Trellis Coded Modulation
CSE 5345 – Fundamentals of Wireless Networks
CT-474: Satellite Communications
II. Modulation & Coding.
Presentation transcript:

1 Today, we are going to talk about: Shannon limit Comparison of different modulation schemes Trade-off between modulation and coding

2 Goals in designing a DCS Goals: Maximizing the transmission bit rate Minimizing probability of bit error Minimizing the required power Minimizing required system bandwidth Maximizing system utilization Minimize system complexity

3 Error probability plane (example for coherent MPSK and MFSK)‏ Bit error probability M-PSK M-FSK k=1,2 k=3 k=4 k=5 k=4 k=2 k=1 bandwidth-efficient power-efficient

4 Limitations in designing a DCS Limitations: The Nyquist theoretical minimum bandwidth requirement The Shannon-Hartley capacity theorem (and the Shannon limit)‏ Government regulations Technological limitations Other system requirements (e.g satellite orbits)‏

5 Nyquist minimum bandwidth requirement The theoretical minimum bandwidth needed for baseband transmission of R s symbols per second is R s /2 hertz.

6 Shannon limit Channel capacity: The maximum data rate at which error-free communication over the channel is performed. Channel capacity of AWGV channel (Shannon- Hartley capacity theorem):

7 Shannon limit … The Shannon theorem puts a limit on the transmission data rate, not on the error probability: Theoretically possible to transmit information at any rate, with an arbitrary small error probability by using a sufficiently complicated coding scheme For an information rate, it is not possible to find a code that can achieve an arbitrary small error probability.

8 Shannon limit … C/W [bits/s/Hz] SNR [bits/s/Hz] Practical region Unattainable region

9 Shannon limit … There exists a limiting value of below which there can be no error-free communication at any information rate. By increasing the bandwidth alone, the capacity can not be increased to any desired value. Shannon limit

10 Shannon limit … W/C [Hz/bits/s] Practical region Unattainable region -1.6 [dB]

11 Bandwidth efficiency plane R<C Practical region R>C Unattainable region R/W [bits/s/Hz] Bandwidth limited Power limited R=C Shannon limit MPSK MQAM MFSK M=2 M=4 M=8 M=16 M=64 M=256 M=2M=4 M=8 M=16

12 Power and bandwidth limited systems Two major communication resources: Transmit power and channel bandwidth In many communication systems, one of these resources is more precious than the other. Hence, systems can be classified as: Power-limited systems: save power at the expense of bandwidth (for example by using coding schemes)‏ Bandwidth-limited systems: save bandwidth at the expense of power (for example by using spectrally efficient modulation schemes)

13 M-ary signaling Bandwidth efficiency: Assuming Nyquist (ideal rectangular) filtering at baseband, the required passband bandwidth is: M-PSK and M-QAM (bandwidth-limited systems)‏ Bandwidth efficiency increases as M increases. MFSK (power-limited systems)‏ Bandwidth efficiency decreases as M increases.

Lecture 1314 Design example of uncoded systems Design goals: 1.The bit error probability at the demodulator output must meet the system error requirement. 2.The transmission bandwidth must not exceed the available channel bandwidth. M-ary modulator M-ary demodulator Input Output

15 Design example of uncoded systems … Choose a modulation scheme that meets the following system requirements:

16 Choose a modulation scheme that meets the following system requirements: Design example of uncoded systems …

17 Design example of coded systems Design goals: 1.The bit error probability at the decoder output must meet the system error requirement. 2.The rate of the code must not expand the required transmission bandwidth beyond the available channel bandwidth. 3.The code should be as simple as possible. Generally, the shorter the code, the simpler will be its implementation. M-ary modulator M-ary demodulator Input Output Encoder Decoder

18 Design example of coded systems … Choose a modulation/coding scheme that meets the following system requirements: The requirements are similar to the bandwidth-limited uncoded system, except that the target bit error probability is much lower.

19 Design example of coded systems Using 8-PSK, satisfies the bandwidth constraint, but not the bit error probability constraint. Much higher power is required for uncoded 8-PSK. The solution is to use channel coding (block codes or convolutional codes) to save the power at the expense of bandwidth while meeting the target bit error probability.

20 Design example of coded systems For simplicity, we use BCH codes. The required coding gain is: The maximum allowed bandwidth expansion due to coding is: The current bandwidth of uncoded 8-PSK can be expanded by still 25% to remain below the channel bandwidth. Among the BCH codes, we choose the one which provides the required coding gain and bandwidth expansion with minimum amount of redundancy.

21 Design example of coded systems … Bandwidth compatible BCH codes Coding gain in dB with MPSK

22 Design example of coded systems … Examine that the combination of 8-PSK and (63,51) BCH codes meets the requirements:

23 Effects of error-correcting codes on error performance Error-correcting codes at fixed SNR influence the error performance in two ways: 1.Improving effect: The larger the redundancy, the greater the error- correction capability 2.Degrading effect: Energy reduction per channel symbol or coded bits for real-time applications due to faster signaling. The degrading effect vanishes for non-real time applications when delay is tolerable, since the channel symbol energy is not reduced.

24 Bandwidth efficient modulation schemes Offset QPSK (OQPSK) and Minimum shift keying Bandwidth efficient and constant envelope modulations, suitable for non-linear amplifier M-QAM Bandwidth efficient modulation Trellis coded modulation (TCM)‏ Bandwidth efficient modulation which improves the performance without bandwidth expansion