Principles of communications

Principles of Communications By: Vinh Dang Quang Course information     Lecturer: Msc Dang Quang Vinh Mail: dg_vinh@yahoo.com Mobile:0983692806 Duration:30 hrs Outline       Basic concepts Information Entropy Joint and Conditional Entropy Channel Representations Channel Capacity Basic concepts What is Information Theory?    Information Theory: how much information  … is contained in a signal?  … can a system generate?  … can a channel transmit? Used in many fields: Communications, Computer Science, Economics,… Examples: Barcelona 0-3 SLNA Information  Let xj be an event with p(xj)  If xj occurred, we have I ( x j ) = log a = − log a p( x j ) p( x j ) units of information  The base of the logarithm     10 →the measure of information is hartley e →the measure of information is nat →the measure of information is bit Examples 10.1 (page 669) Entropy     H(X) = - ∑ p(x) log p(x) Entropy = information = uncertainty If a signal is completely predictable, it has zero entropy and no information Entropy = average number of bits required to transmit the signal Entropy example         Random variable with uniform distribution over 32 outcomes H(X) = - ∑ 1/32 log 1/32 = log 32 = # bits required = log 32 = bits! Therefore H(X) = number of bits required to represent a random event How many bits are needed for: Outcome of a coin toss “tomorrow is a Wednesday” US tops Winter Olympics tally” Entropy example  Horse race with horses, with winning probabilities ½, ¼, 1/8, 1/16, 1/64, 1/64, 1/64, 1/64 Entropy H(X) = bits  How many bits we need?  (a) Index each horse  log8 = bits  (b) Assign shorter codes to horses with higher probability: 0, 10, 110, 1110, 111100, 111101, 111110, 111111  average description length = bits!  Entropy    Need at least H(X) bits to represent X H(X) is a lower bound on the required descriptor length Entropy = uncertainty of a random variable Joint and conditional entropy Joint entropy: H(X,Y) = ∑x ∑y p(x,y) log p(x,y)  simple extension of entropy to RVs  Conditional Entropy: H(Y|X) = ∑x p(x) H(Y|X=x) = ∑x ∑y p(x,y) log p(y|x) “What is uncertainty of Y if X is known?”  Easy to verify:     If X, Y independent, then H(Y|X) = H(Y) If Y = X, then H(Y|X) = H(Y|X) = extra information between X & Y Fact: H(X,Y) = H(X) + H(Y|X) Mutual Information  I(X;Y) = H(X) – H(X|Y) = reduction of uncertainty due to another variable      I(X;Y) = ∑x ∑y p(x,y) log p(x,y)/{p(x)p(y)} “How much information about Y is contained in X?” If X,Y independent, then I(X;Y) = If X,Y are same, then I(X;Y) = H(X) = H(Y) Symmetric and non-negative Mutual Information Relationship between entropy, joint and mutual information Mutual Information     I(X;Y) is a great measure of similarity between X and Y Widely used in image/signal processing Medical imaging example:  MI based image registration Why? MI is insensitive to gain and bias [...]... reduction of uncertainty due to another variable      I(X;Y) = ∑x ∑y p(x,y) log p(x,y)/{p(x)p(y)} “How much information about Y is contained in X?” If X,Y independent, then I(X;Y) = 0 If X,Y are same, then I(X;Y) = H(X) = H(Y) Symmetric and non-negative Mutual Information Relationship between entropy, joint and mutual information Mutual Information     I(X;Y) is a great measure of similarity ... p( x j ) p( x j ) units of information  The base of the logarithm     10 →the measure of information is hartley e →the measure of information is nat →the measure of information is bit Examples... bits required = log 32 = bits! Therefore H(X) = number of bits required to represent a random event How many bits are needed for: Outcome of a coin toss “tomorrow is a Wednesday” US tops Winter... descriptor length Entropy = uncertainty of a random variable Joint and conditional entropy Joint entropy: H(X,Y) = ∑x ∑y p(x,y) log p(x,y)  simple extension of entropy to RVs  Conditional Entropy: