文件名称:
Mathuranathan_Viswanathan_SIMULATION_OF.pdf
开发工具:
文件大小: 10mb
下载次数: 0
上传时间: 2019-10-13
详细说明：有关于数字通信的知识，内容覆盖了一些仿真代码，有对此有兴趣的人可以看看。commercial use of the work under the following conditions: 1)You must attribute the work in the
manner specified by the author (but not in any way that suggests that they endorse you or your use of
the work) and 2)If you alter, transform, or build upon the programming snippets, you may distribute
the resulting work only under the same or similar license to this one
Any of the above conditions can be waived if you get permission from the author
The Author claims no responsibility for the persistence or accuracy of URls of external or third-party
internet websites referred to in this publication, and does not guarantee that any content on such
websites is, or will remain, accurate or appropriate
水水米水水
This ebook is meant for students and instructors who are interested in simulation of signal processing
and digital communication with Matlab. You should have a fair understanding of Matlab programming
to begin with. Essential topics in digital communication are introduced to foster understanding of
simulation methodologies. References are given in square brackets with in the text Please refer the
last section on references to get more details. The following manuscript is a result of five years of
author's work and you are welcome to give feedback to make it better. Please check authors page
(given at the end of this book) for contact info
Acknowledgement: Thanks to Varsha Mathuranathan for editing and proof-reading this ebook
SIMULATION OF DIGITAL COMMUNICATION SYSTEMS USING MATLAB
Table of contents
Chapter 1: Essentials of Digital Communication
Introduction to Digital Communi cation
1.2 Sampling Theorem-Baseband Sampling
3 Sampling Theorem- Bandpass or Intermediate or Under Sampling
1.4 Oversampling ADC-DAC Conversion, pulse shaping and Matched Filter
1.5 Channel Capacity
1.6 Performance of channel codes
1.7 Distances: Hamming Vs Euclidean
8 Hard and soft decision decoding
1.9 Maximum likelihood decoding
Chapter 2: Channel Coding
Hamming codes How it works
2.2 Construction of hamming codes using matrices
2. 3 Introduction to reed Solomon Codes
2. 4 Block Interleaver Design for RS codes
2.5 Convolutional coding and viterbi decoding
Chapter 3: Inter Symbol Interference and Filtering
3.1 Introduction to controlled ISI(Inter Symbol Interference
3.2 Correlative coding-Duobinary Signaling
3. 3 Modified Duobinary signaling
3. 4 Raised cosine filter
3.5 Square Root Raised Cosine Filter (Matched/split filter implementation
3.6 Gibbs phenomena -a demonstration
3. 7 Moving Average (MA) Filter
Chapter 4: Probability and Random Process
4. 1 Introduction to concepts in probability
4.2 Baves'Theorem
4.3 Distributions and Density Functions
4.4 Gaussian random variable and Gaussian distribution
4.5 Uniform random variables and Uniform distribution
4. 6 Chi-Squared Random Variable and Chi-Squared Distribution
4. 7 Non-central Chi-squared Distribution
4.8 Central Limit theorem
4.9 Colored Noise generation in matlab
Chapter 5: Channel Models and Fading
5.1 Introduction to channel models
5.2 Friis Free Space Propagation Model
5.3 Log Distance path loss or log normal shadowing model
5. 4 Hata- Okumura Models
5.5 Introduction to Fading models
5.6 Rayleigh Fading and Rayleigh Distribution
5.7 Rayleigh Fading Simulation- Young's model
5.8 Simulation of Rayleigh Fading Model-( Clarke's Model-Sum of sinusoids)
5.9 Rician fading and rician distribution
Chapter 6: Digital Modulations
6.1 BPSK Modulation and demodulation
6.2 bER vs. Eb/No for BPsK modulation over AWGN
6.3 Eb/NO vS, BER for BPsK over Ravleigh Channel
6.4 Eb/No Vs ber for bpsk over rician fading channel
6.5 OPSK Modulation and demodulation
6. 6 BER VS. Eb/NO for QPSK modulation over AWGN
6. 7 bER vs, Eb/No for 8-PSK Modulation over AwGn
6.8 Simulation of m-psk modulations over awgn
6.9 Symbol Error Rate vs SNR performance curve simulation for 16-QAM
6. 10 Symbol Error Rate Vs SNR performance curve simulation for 64-QAM
6. 11 Performance comparison of Digital Modulation techniques
6. 12 Intuitive derivation of Performance of an optimum BPSK receiver in AWGN
channe
Chapter 7: Orthogonal Frequency Division Multiplexing(OFDM
7.I Introduction to OfDm
7.2 Role of fftifft in ofdm
1.3 Role of Cyclic Prefix in OFDM
7.4 Simulation of OFDM system in Matlab- BER Vs Eb/NO for OFDM in AWGN channel
Chapter 8: Spread Spectrum Techniques
Introduction to Spread Spectrum Communication
8.2 Codes used in CDma
8.3 Maximum Length Sequences(m-sequences
8. 4 Preferred Pairs m-sequences generation for Gold Codes
8.5 Generation of Gold Codes and their cross-correlation
Appendix
Al: Deriving Shannon-Hartley Equation for CCMC AWGN channel-Method 1
A2. Capacity of Continuous input Continuous output Memoryless AWGN-Method 2
A3: Constellation Constrained Capacity of M-ary Scheme for AWGN channel
A4: Natural and Binary Codes
A5: Constructing a rectangular constellation for 16QAM
A6: Q Function and Error Function
References
About the author
End of table of contents
Chapter 1: Essentials of Digital Communication
1.1 Introduction to Digital Communication
Goals of Communication System design:
Digital communication involves transmission of messages using finite alphabets (finite symbols)
during finite time intervals( finite symbol interval). Any communication system(be it analog or digital
in nature) in the electronic consumer market(be it hard disk drives, Compact Discs, telephony,
mobile communication systems, etc., is made up of the following elements as represented in
following figure
Source
(User)
Source
Encoder
Channel modulator
Encoder
Channel
(Medium)
Destination
(User)
Source
Channel
Decoder
Demodulator
Decoder
The prime goals of a communication design engineer(one who designs a practical communication
system) would be to
D Reduce the bandwidth needed to send data
Bandwidth, a limited and valuable resource is the difference between the highest and the lowest
frequency allocated for transmi tting a message in any communication system. For example in GSm
technology the typical bandwidth allocated for a single user is 200 KHz. More bandwidth provides
space to transmit more data as well as more transmission rate(measured in bits per second -"bps)
The goal of reduced bandwidth is needed because of the growing bandwidth demands and the limited
availability of communication spectrum. a downloading speed of 56Kbps was felt sufficient few
years ago, but now it is not so. Hence it is essential to send more data in lesser bandwidth. This is
achieved by compressing the data at the transmitting end and decompressing it at the receiving end. a
Source encoder”anda“ Source decoder” serve this purpose.
2) To make data robust against harsh environments
phones are operated in a very noisy environment in which the noise sources may be one or more or o
Data will get corrupted when it is sent in harsh media (referred to as"channel). For example mobile
the following: interference from other mobile users, ignition noise, thermal noise, multipath
interference and other man made noises Channel coding is a technique to make the transmitted data
robust to such noises, meaning that you can still recover your data(using a channel decoder) intact
even if it is corrupted by certain amount of noise
3) Send data over a long distance
Obviously data has to be sent over a long distance through any media used for/by the communication
system. The media may be a simple twisted pair copper wires used in telephone networks or the air
media in the case of a mobile or satellite communication system. In the physical world it is not
possible to send a signal (carrying data over infinite distance. According to the inverse square law
of distance the intensity of the transmi tted signal is inversely proportional to the square of the
distance
1
Signal intensity∝
distance
he inverse square law of distance works at every nook and corner of the world to increasingly
attenuate the signals intensity over the distance and eventually kills the signal completely. Data can
travel long distances if it has more energy. Now the challenge is to increase the energy of the signal
so that it can travel the intended long distance
A signal sent over a medium is essentially an electromagnetic wave. According to Planck-Einstein
equation, the energy of a photon and the frequency of the associated electromagnetic wave are related
by
E= ju
where e= energy of the transmitted signal, h-Planck's cons tant and frequency of transmission
The above mentioned equation implies that the energy of the signal can be increased by increasing the
frequency of transmission. Equivalently the frequency of the data has to be shifted from lower
frequency region to higher frequency region. This is achieved by Modulation. Demodulation is the
complementary operation that restores the original frequency contents of a message
Source Coding and de coding
Source coding, the first block in the communication system architecture shown in the previous figure
is the process of encoding the information using lesser number of bits than the uncoded version of the
information. Essentially it is the other name for compression. All data compression techniques can be
classified under two categories namely lossless compression techniques and lossy compression
techniques. In lossless compression the exact original data can be reconstructed from compressed
data. But in lossy compression some errors exist after de-compression, but those errors are not
obvious or perceivable. A Few lossless and lossy compression techniques are listed below
osSless compression Techniques
LZW (Lempel Ziv Welch) coding- algorithm used in PdF documents [zivMay19771
[ZivSep1977],[Welch1985]
2)Huffman coding [huff1952-used widely as the final coding stage
3)Shannon-Fano coding [Fano1949-used in IMPLODE compression method used for ZiP file
formats
Run Length encoding [Golomb 1966]-used in FAX machines
5)Golomb Coding-used in image compression -(implemented in Rice Algorithm for image
compression) Ricel9791
Lossy Compression Techniques:
JPEG [William1993]-Image compression technique(an implementation of Discrete Cosine
Transform (DCT))
2)MPEG [WebMPEG]- Motion picture compression technique
3)A-Law and Mu-Law compression [WebITUG711]- Used in Audio compression
4) Linear Predictive Coding(LPC)-Used in Speech signal processing [Deng2003]
5)RELP(Residually Excited LPC), CELP(Codebook Excited LPC)-variants of LPC used in GSm
and CDMa for voice compression
Channel coding and Decoding:
he next block in a communication system is the channel coding block. There is an important
difference between channel coding and source coding Source coding attempts to compress the data to
improve bandwidth utilization, whereas, channel coding attempts to add redundancy to the data to
make it more reliable(which reduces data rate) and therefore more robust against the channel noise
Channel coding reduces the data rate and improves the reliability of the system
Steps in Channel Coding design
1) Identi fy the Channel or the medium of communication
2) Model the channel according to its nature or choose from pre-defined models which best suits the
actual environment
3)Decide over the type of coding strategy which will give best/required performance resul ts b
pertorming simul ations
Some channel models.
Several models of channels were developed to design a communi cation s ystem according to the
possible type of channel one may use. two of them are listed here
Binary Symmetric Channel (bsc):
In this model, the transmitter sends a bit and the receiver receives it. Suppose if there exists a
probability for this bit getting flipped, then it is referred to as a Binary Symmetric Channel. Under this
model, the probability oferroneous reception isp and the probability of correct reception is given
by l-p. This situation can be diagrammatically represented as shown in following figure
1
0
p
Transmitter
Receiver
Y
1
1
Given the transmitted bit represented by X' and received bit represented by y, in terms of
conditional probability, the Binary Symmetric Channel can be represented as
P(Y=0K=0)=1-P
PY=0x=1)=
P(Y=1|x=0)
P(Y=1X=1)=1-P
The above conditional probability specifies that the probability of erroneous reception ( sent X=0 and
received Y=1 or vice versa)is‘p’ and the probability of correct reception is‘1-p’
Additive White Gaussian Noise Channel (AWGn:
In this model, the channel noise is assumed to have Gaussian nature and is additive. Compared to
other equivalent channels the awgn channel does the maximum bit corruption and the systems
designed to provide reliability in aWgn channel is assumed to give best performance results in
other real-world channels. but the real performance may vary. The awgn channel is a good model
for many satellite and deep space communication links. In serial data communications, the aWGn
mathematical model is used to model the timing error caused by random jitter. The distortion incurred
by transmission over a lossy medium is modeled as the addition of a zero-mean gaussian random
value to each transmitted bit
Channel Coding Design Approach
The design approach that is widely used is called Forward Error Correction (FEC). This error
correction technique is used to send data over unreliable noisy channels. The transmitted information
is added with redundant bits using Error Correction Coding(ECC), otherwise called"channel
coding,. This approach allows us to detect and correct the bit errors in the receiver without the need
for retransmission. It is important to bear in mind that the correction and detection of errors are not
absolute but rather statistical. Thus, one of our goals is to minimize the ber (Bit Error Rate)given a
channel with certain noise characteristics and bandwidth
In this method K original bits which are also called informational bits are replaced with n>k new
bits called"coded bits"code words". The difference N-K represents the number of redundant
bits added to the informational bits. Error Control Coding techniques are used to produce the code
words from the information bits. The codewords carry with them an inherent potential (to certain
extent) to recover from the distortions induced by the channel noise. The corresponding decoding
technique in the receiver uses the redundant information in the codeword and tries to restore the
original information thereby providing immuni ty against the channel noise there are two general
schemes for channel coding: Linear Block Codes and(linear)Convolution Codes. There exist even
other sophisticated schemes/categories like Trellis Coded Modulation ( TCm)which combines both
the channel encoder and parts of the modulator into a single entity, non-linear channel coding
techniques etc. To keep things simple, we will not go into the jungle of advanced channel coding
techniques
水冰半冰水水
Back to Table of contents
1.2 Sampling Theorem- Baseband sampling
Nyquist-Shannon Sampling Theorem? is the fundamental base over which all the digital processing
techniques are built
Processing a Sig
nal in digital domain gives several advantages (like immunity to temperature drift,
accuracy, predictability, ease of design, ease of implementation etc., ) over analog domain
processing
Analog to Digital conversion:
In analog domain, the signal that is of concern is continuous in both time and amplitude. The process
of discretization of the analog signal in both time domain and amplitude levels yields the equivalent
digital signal
The conversion of analog to digital domain is a three step process
Discretization in time- Sampling
2) Discretization of amplitude levels-Quantization
3)Converting the discrete samples to digital samples-Coding/Encoding
Analog
,
ADC
Digital
Signal
81
ampler
Quantizer
Coder
The sampling operation samples("chops")the incoming signal at regular intervals called"Sampling
Rate"(denoted by Ts Sampling Rate is determined by Sampling Frequency(denoted by Fs as
Lets consider the following logical questions
k Given a real world signal, how do we select the sampling rate in order to faithfully represent the
signal in digital domain?
ck Are there any criteria for selecting the sampling rate?
k Will there be any deviation if the signal is converted back to analog domain?
Answer: Consult the "Nyquist-Shannon Sampling Theorem?"to select the sampling rate or sampling
frequency
Nyquist-Shannon Sampling Theorem:
The follow ing sampling theorem is the exact reproduction of text from Shannon's classic paper
IShannon1949]
Ifa function f(t) contains no frequencies higher than w cps, it is completely determined by giving
ordinates at a series of points spaced 1/2W seconds apart
Sampling Theorem mainly falls into two categories
1)Baseband Sampling- Applied for signals in the baseband(useful frequency components extending
from OHz to some Fm hz
2) Band pass Sampling- Applied for signals whose frequency components extent from some FI Hz to
F2Hz(where F2>F)
(系统自动生成,下载前可以参看下载内容)
下载文件列表
相关说明
- 本站资源为会员上传分享交流与学习,如有侵犯您的权益,请联系我们删除.
- 本站是交换下载平台，提供交流渠道，下载内容来自于网络，除下载问题外，其它问题请自行百度。
- 本站已设置防盗链，请勿用迅雷、QQ旋风等多线程下载软件下载资源，下载后用WinRAR最新版进行解压.
- 如果您发现内容无法下载，请稍后再次尝试；或者到消费记录里找到下载记录反馈给我们.
- 下载后发现下载的内容跟说明不相乎，请到消费记录里找到下载记录反馈给我们，经确认后退回积分.
- 如下载前有疑问，可以通过点击"提供者"的名字，查看对方的联系方式，联系对方咨询.
