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WorksheetsDIGITAL COMMUNICATION
Total questions: 20
Worksheet time: 13mins
The expected information contained in a message is called
Entropy
Efficiency
Coded signal
None of the above
the memory less source refers to
No previous information
No message storage
Emitted message is independent of previous message
None of the above
For a binary symmetric channel, the random bits are given as
Logic 1 given by probability P and logic 0 by (1-P)
Logic 1 given by probability P and logic 0 by (1-P)
Logic 1 given by probability P2 and logic 0 by 1-P
Logic 1 given by probability P and logic 0 by (1-P)2
The channel capacity according to Shannon's equation is
Maximum error free communication
Defined for optimum system
Information transmitted
All of the above
For M equally likely messages, M>>1, if the rate of information R > C, the probability of error is
Arbitrarily small
Close to unity
Not predictable
Unknown
The negative statement for Shannon's theorem states that
If R > C, the error probability increases towards Unity
If R < C, the error probability is very small
None of the above
Not applicable
According to Shannon Hartley theorem,
the channel capacity becomes infinite with infinite bandwidth
channel capacity does not become infinite with infinite bandwidth
as a tradeoff between bandwidth and Signal to noise ratio
Both b) and c) are correct
The capacity of a binary symmetric channel, given H(P) is binary entropy function, is
1-H(P)
1-H(P)2
The channel capacity is
The maximum information transmitted by one symbol over the channel
Information contained in a signal
The amplitude of the modulated signal
All of the above
For M equally likely messages, the average amount of information H is
H= log10M
H= log2M
H= log10M2
H= 2log10M
The capacity of Gaussian channel is
C= 2B(1+S/N) bits/s
C= B2(1+S/N) bits/s
C= B(1+S/N) bits/s
C= B(1+S/N)2 bits/s
The expected information contained in a message is called
Entropy
Efficiency
coded signal
None of the above
The information I contained in a message with probability of occurrence is given by (k is constant)
I = k log21/P
. I = k log2P
. I = k log21/2P
. I = k log21/P2
The relation between entropy and mutual information is
I(X;Y) = H(X) – H(X/Y)
I(X;Y) = H(X/Y) – H(Y/X)
I(X;Y) = H(Y) – H(X)
I(X;Y) = H(Y) – H(X)
The mutual information is
Is symmetric
Always non nagative
Both a and b correct
None of the above
For a binary symmetric channel, the random bits are given as
Logic 1 given by probability P and logic 0 by (1-P)
Logic 1 given by probability 1-P and logic 0 by P
Logic 1 given by probability P2 and logic 0 by 1-P
Logic 1 given by probability P and logic 0 by (1-P)2
The channel capacity according to Shannon’s equation is
Maximum error free communication
Defined for optimum system
Information transmitted
All of the above
The technique that may be used to increase average information per bit is
Shannon fano algorithm
ASK
FSK
Digital modulationntechniques
The unit of average mutual information is
Bits
Bytes
Bits per symbol
Bytes per symbol
When probability of error during transmission is 0.5, it indicates that
Channel is very noisy
No information is received
Channel is very noisy & No information is received
None of the mentioned
