《通信原理实验》课程电子教案（PPT讲稿）MATLAB与通信仿真（英文）Chapter 3 Analog-to-Digital Conversion（Preview、Measure of Information、Quantization）

Analog-to-Digital Conversion Preview Measure of Information Quantization

Analog-to-Digital Conversion Preview Measure of Information Quantization

Why ADC Many sources are Analog by nature Speech,Image,Telemetry sources,. ■Digital is easier ■To process ■To communicate ■To store

Why ADC ? ◼ Many sources are Analog by nature ◼ Speech, Image, Telemetry sources, . ◼ Digital is easier ◼ To process ◼ To communicate ◼ To store

Data Compression Quantization (Lossy data compression) Analog source is quantized into a finite number of levels Limit on the performance is given by rate-distortion bound Noiseless coding(Lossless data compression) Compress digital data to reduce data bit Source coding techniques Limit on the compression is given by the entropy of the source

Data Compression ◼ Quantization (Lossy data compression) ◼ Analog source is quantized into a finite number of levels ◼ Limit on the performance is given by rate-distortion bound ◼ Noiseless coding(Lossless data compression) ◼ Compress digital data to reduce data bit ◼ Source coding techniques ◼ Limit on the compression is given by the entropy of the source

Common-sense Information Measure Consider three headlines in newspaper 1)Tomorrow the sun will rise in the east ■ 2)United States invades Cuba 3)Cuba invades United States Information surprise ~1/Probability Headline Surprise Probability Information 1 Never Always No 2 Much Small Much 3 Very Much Very Small Very Much Suggested Information Measure I oclog p

Common-sense Information Measure ◼ Consider three headlines in newspaper ◼ 1) Tomorrow the sun will rise in the east ◼ 2) United States invades Cuba ◼ 3) Cuba invades United States ◼ Information ~ surprise ~ 1/Probability ◼ Suggested Information Measure ◼ 1 I log P  Headline Surprise Probability Information 1 Never Always No 2 Much Small Much 3 Very Much Very Small Very Much

Engineering-sense Information Measure From the engineering point of view The information in a message is the time to transmit the message Message with higher probability can be transmitted in a shorter time than that required for a message with lower probability ■Morse Code Shorter code word for symbol e,t,a,o which occurs more frequently Longer code word for symbols x,k,q,z which occurs less frequently The time required to transmit a symbol with probability of occurrence P is proportional to log(1/P)

Engineering-sense Information Measure ◼ From the engineering point of view ◼ The information in a message is the time to transmit the message ◼ Message with higher probability can be transmitted in a shorter time than that required for a message with lower probability ◼ Morse Code ◼ Shorter code word for symbol e,t,a,o which occurs more frequently ◼ Longer code word for symbols x,k,q,z which occurs less frequently ◼ The time required to transmit a symbol with probability of occurrence P is proportional to log(1/P)

Definition of Information Measure The information sent from a digital source when the jth message transmitted is given by "I,=lg(分bs The Unit [bits] Binary Unit Since the base 2 logarithm is used 。Unit of information Do not confuse the bit used in computer(unit of binary data)

Definition of Information Measure ◼ The information sent from a digital source when the jth message transmitted is given by ◼ ◼ The Unit [bits] ◼ Binary Unit ◼ Since the base 2 logarithm is used ◼ Unit of information ◼ Do not confuse the bit used in computer (unit of binary data) 2 1 log ( ) [ ] j j I bits P =

Definition of Entropy The average information measure(or Entropy)of digital source is ·n-2P以，-豆Pg白a网 Where m is number of possible different source message e For the binary message with probability p and(1-p) The Entropy is ·从-2gg分=pe方0-pa2 See Figure 4.1 at page 132 mlfp→1orp→0，there are no information p=0.5 gives maximum information

Definition of Entropy ◼ The average information measure(or Entropy) of digital source is ◼ ◼ Where m is number of possible different source message ◼ For the binary message with probability p and (1-p), The Entropy is ◼ ◼ See Figure 4.1 at page 132 ◼ If p→1 or p→0, there are no information ◼ p=0.5 gives maximum information 2 1 1 1 log ( ) [ ] m m j j j j j j H P I P bits = = P = =   2 2 2 2 1 1 1 1 log ( ) log (1 )log 1 b j j j H P p p = P p p = = + − − 

Channel capacity Is it possible to invent a system with no bit error at the output even when channel noise exists Shannon showed that Yes For AWGN case,if C which satisfies,can be found R=7≤G=slg,0+3 Source rate SNR Entropy Channel BW Time to send message Channel Capacity But,he dose not show us how to build this system

Channel capacity ◼ Is it possible to invent a system with no bit error at the output even when channel noise exists ? ◼ Shannon showed that Yes ◼ For AWGN case, if C which satisfies, can be found ◼ ◼ But, he dose not show us how to build this system 2 log (1 ) H S R C B T N =  = + SNR Channel BW Channel Capacity Source rate Entropy Time to send message

Noiseless coding Reduce number of bits required for the representation of a source output for perfect recovery Prefect reconstruction of source is possible to use a code with a rate as close to the entropy of the source,but it is not possible to use a code with a rate less than a source entropy Huffman coding and Lempel-Ziv coding are popular

Noiseless coding ◼ Reduce number of bits required for the representation of a source output for perfect recovery ◼ Prefect reconstruction of source is possible to use a code with a rate as close to the entropy of the source, but it is not possible to use a code with a rate less than a source entropy ◼ Huffman coding and Lempel-Ziv coding are popular

Huffman coding Assign longer code word to the less probable source output and shorter code word to the more probable ones lllustrative problem 3.1 See figure 3.2 for Huffman code tree Entropy of the source H=3.0371 Average code word length L=3.1 bits/source output ■H<L

Huffman coding ◼ Assign longer code word to the less probable source output and shorter code word to the more probable ones ◼ Illustrative problem 3.1 ◼ See figure 3.2 for Huffman code tree ◼ Entropy of the source H = 3.0371 ◼ Average code word length L = 3.1 bits/source output ◼ H < L