Principles of Information Science Chapter 5 Principles of Information Transferring Communication Theory
Principles of Information Science Chapter 5 Principles of Information Transferring: Communication Theory
1. Model of Communication System noise Source Sink T Channel T. Transformation Source, sink, and channel are given beforehand. Transformation: to match the source with the channel
1. Model of Communication System T C T Source Sink Channel Transformation: to match the source with the channel. Source, sink, and channel are given beforehand. Noise X Y -1 T: Transformation
The functions of Transformation Modulation: Spectra Matching, Better performance/cost seekin Amplification: Signal/Noise Improving Equalization: Channel Characteristic Adjusting Source Coding: Transmission Efficiency Bettering Channel coding: Noise Immunity Cryptographic Coding: Security Protection
The Functions of Transformation - Modulation: Spectra Matching, Better Performance/Cost Seeking - Amplification: Signal/Noise Improving - Equalization: Channel Characteristic Adjusting - Source Coding: Transmission Efficiency Bettering - Channel Coding: Noise Immunity - Cryptographic Coding: Security Protection
2. Model Analysis a radical feature of communication. The sent waveform recovery at receiving end with a certain fidelity under noises. Ignoring the content and utility factors, the model of communication exhibits statistical pl roperties Source Entropy: H(X), HX, Y) Mutual Information I(X; Y)=H(X)-H(XY) H(Y-H(YX
2, Model Analysis Ignoring the content and utility factors, the model of communication exhibits statistical properties. Source Entropy: H(X), H(X, Y) I(X; Y) = H(X) - H(X|Y) = H(Y) - H(Y|X) A radical feature of communication: The sent waveform recovery at receiving end with a certain fidelity under noises. Mutual Information:
Define Channel Capacity C Max p(X}1(X;Y) Example(AWGn Channel) p(yx)=(22)exp(1/2a2)(y-x) (X; Y)=H(Y-H(YX) H(Y-p(x) p(ylx)log pyx) dy dx H(Y)-log(2Toe 1/2 The only way to maximize I(X; Y)is to maximize HY) This requires y being normal variable with zero mean Due to Y=X+N, it requires X being a normal variable with zero mean
Channel Capacity C = I(X; Y) Max Define {p(X)} Example (AWGN Channel): p(y|x) =(2ps ) exp[-(1/2s ) (y-x) ] = H(Y) - p(x) p(y|x) log p(y|x) dy dx 2 = H(Y) - log (2ps e) I(X; Y) = H(Y) - H(Y|X) The only way to maximize I(X; Y) is to maximize H(Y). This requires Y being normal variable with zero mean. Due to Y = X+N, it requires X being a normal variable with zero mean. -1/2 2 2 - - 2 1/2
Let Py=p+oP C= log(2TePy-log(2ceo 2) (1/2)log(Py/o2) (12)log(1+P/N) (bit/symbol) If x is a white gaussian signal with bandwidth f and duration T, then there are 2FT symbols transmitted per second. Therefore, we have C=FT log(1+P/ bit/second the famous capacity formula
Let P = P + s C = log (2peP ) Y - log(2pes ) 1/2 1/2 = (1/2) log (P /s ) 2 Y = (1/2) log (1 + P/N) (bit/symbol) If X is a white Gaussian signal with bandwidth F and duration T, then there are 2FT symbols transmitted per second. Therefore, we have C = FT log (1 + P/N) bit/second, 2 Y 2 the famous capacity formula. (N = s ) 2
From C=FT log(1+P/N) A)F, T, P are basic parameters of a channel Signal volume to be transmitted through a channel must be smaller than the channel capacity provided
F F T T P P Signal Volume to be transmitted through a channel must be smaller than the channel capacity provided. From C = FT log (1 + P/N ) A) F, T, P are basic parameters of a channel
B)For a given C, the parameters are exchangeable. This provides great flexibility for communication systems designing. C)Signal Division Frequency Division -- FDMA Time Division - TDMA Time Frequency Division Frequency Hopping
C) Signal Division Frequency Division -- FDMA Time Division -- TDMA Time Frequency Division -- Frequency Hopping B) For a given C, the parameters are exchangeable. This provides great flexibility for communication systems designing. F T P Channel Capacity
D)It is the origin of CDMa For a given channel with additive white gaussian noise, the maximal value of the capacity can be implemented when the signal form is also additive white gaussian in nature noise-like signal. This lead to the noise communication and then the pseudo-noise coding communication, or code multiple access- CDMA CDMA: each user is assigned a different, and also mutual orthogonal, code as its address
D) It is the origin of CDMA For a given channel with additive white Gaussian noise, the maximal value of the capacity can be implemented when the signal form is also additive white Gaussian in nature -- noise-like signal. This lead to the noise communication and then the pseudo-noise coding communication, or code multiple access -- CDMA. CDMA: each user is assigned a different, and also mutual orthogonal, code as its address
3, H(X) Analysis: Source Coding Source Encoder For being a ble to express the amount of information of a source with H(X), the encoder with an average length, L, of codes should keep the relation as below: H(X)=-(x)log p(x )<l plx l Otherwise, distortion will unavoidably be introduced. Thus, the optimal coding is the one with
3, H(X) Analysis: Source Coding For being able to express the amount of information of a source with H(X), the encoder with an average length, l, of codes should keep the relation as below: Otherwise, distortion will unavoidably be introduced. H(X) = - p(x ) log p(x ) < l = p(x ) l Thus, the optimal coding is the one with Source Encoder X Y n n - n=1 N - n=1 N n n
