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Chapter: Communication Theory - Information Theory

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Important Questions and Answers: Information Theory

Communication Theory - Information Theory - Important Questions and Answers: Information Theory

INFORMATION THEORY

 

1. What is entropy?

Entropy is also called average information per message. It is the ratio of total information to number of messages. i.e.,

Entropy, H = Total information / Number of messages

 

2. What is channel redundancy?

Redundancy = 1 – code efficiency

Redundancy should be as low as possible.

 

3. Name the two source coding techniques.

 The source coding techniques are, a) prefix coding b)

 Shannon-fano coding c) Huffman coding

 

4. Write the expression for code efficiency in terms of entropy.

Code efficiency = Entropy(H) / Average code word length(N)

 

5. What is memory less source? Give an example.

The alphabets emitted by memory less source do not depend upon previous alphabets. Every alphabet is independent. For example a character generated by keyboard represents memory less source.

 

6. Explain the significance of the entropy H(X/Y) of a communication system where X is the transmitter and Y is the receiver.

a) H(X/Y) is called conditional entropy. It represents uncertainty of X, on average, when Y is known.

b) In other words H(X/Y) is an average measure of uncertainty in X after Y is received.

c) H(X/Y) represents the information lost in the noisy channel.

 

7. What is prefix code?

In prefix code, no codeword is the prefix of any other codeword. It is variable length code. The binary digits (codewords) are assigned to the messages as per their probabilities of occurrence.

 

8. Define information rate.

Information rate(R) is represented in average number of bits of information per second. It is calculated as,

R = r H Information bits / sec

 

9. Calculate the entropy of source with a symbol set containing 64 symbols each with a probability pi = 1/ 64 .

Here, there are M = 64 equally likely symbols. Hence entropy of such source is given as,H = log 2 M = log 2 64 = 6 bits / symbol

10. State the channel coding theorem for a discrete memory less channel.

Statement of the theorem:

 

Given a source of ‗M‘ equally likely messages, with M >>1, which is generating information at a rate. Given channel with capacity C. Then if,R ≤ C

 

There exits a coding technique such that the output of the source may be transmitted over the channel with a probability of error in the received message which may be made arbitrarily small.

 

Explanation: This theorem says that if R ≤ C ; it is possible to transmit information without any error even if noise is present. Coding techniques are used to detect and correct the errors.

 

11. What is information theory?

 

Information theory deals with the mathematical modeling and analysis of a communication system rather than with physical sources and physical channels

 

12. Explain Shannon-Fano coding.

An efficient code can be obtained by the following simple procedure, known as Shannon

 

– Fano algorithm.

Step 1: List the source symbols in order of decreasing probability.

 

Step 2: Partition the set into two sets that are as close to equiprobable as possible, and sign 0 to the upper set and 1 to the lower set.

 

Step: Continue this process, each time partitioning the sets with as nearly equal probabilities as possible until further partitioning is not possible.

 

13. Define bandwidth efficiency.

The ratio of channel capacity to bandwidth is called bandwidth efficiency. i.e,

 

Bandwidth efficiency = Channel Capacity / Bandwidth (B)

 

14. Define channel capacity of the discrete memory less channel.

 

The channel capacity of the discrete memory less channel is given as maximum average mutual information. The maximization is taken with respect to input probabilities.

 

 

GLOSSARY TERMS:

 

1.     Entropy, the average amount of information per source symbol.

 

2.     Information, is a continuous function of probability,

 

3.     Channel, is the medium through which the information is transmitted from the source to destination.

 

4.     Channel capacity, maximum of mutual information that may be transmitted through the channel.

 

5.     Binary symmetric channel, the channel is symmetric because the probability of receiving a '1‘ if a '0‘ is transmitted is same as the probability of receiving a '0‘ if '1‘ is transmitted.

 

6.     SNR, the ratio of input noise is divided by output noise.

 

 

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