Suppose the bit detection sample at the receiver is V + noise
volts when the sample corresponds to a transmitted '1', and
0.0 + noise volts when the sample corresponds to a
transmitted '0', where noise is a zero-mean
Normal(Gaussian) random variable with standard deviation σNOISE.
If the transmitter is equally likely to send '0''s or '1''s,
and V/2 volts is used as the threshold for deciding whether
the received bit is a '0' or a '1', give an expression for the
bit-error rate (BER) in terms of the erfc function and
σNOISE.
Here's a plot of the PDF for the received signal
where the red-shaded areas correspond to the probabilities of receiving
a bit in error.
so the bit-error rate is given by
0.5*erfc(V/(sqrt(8)*sigma)). Note that sigma = sqrt(N0/2) using the definition of N0. This formula is related to the 0.5*sqrt(E_s/N0) from Chapter 5 and the lecture; E_s in our case is V*V/4.
Suppose the transmitter is equally likely to send zeros or ones and
uses zero volt samples to represent a '0' and
one volt samples to represent a '1'. If the receiver uses 0.5 volts
as the threshold for deciding bit value, for what value of
σNOISE is the probability of a bit error
approximately equal to 1/5?
From part (A), 0.5*erfc(1/(sqrt(8)*σNOISE)) = 0.2, which gives us σNOISE = 0.594.
Will your answer for σNOISE in part (B) change
if the threshold used by the receiver is shifted to 0.6 volts? Do not
try to determine σNOISE, but justify your answer.
If move Vth higher to 0.6V, we'll be decreasing prob(rcv1|xmit0) and
increasing prob(rcv0|xmit1). Considering the shape of the Gaussian
PDF, the decrease will be noticeably smaller than the increase, so
we'd expect BER to increase for a given σNOISE. Thus
to keep BER = 1/5, we'd need to decrease our estimate for
σNOISE. One can also work out the same result with some algebra; we saw in Chapter 6 how picking the mid-point threshold minimizes the bit error rate.
Will your answer for σNOISE in part (B) change if the
transmitter is twice as likely to send ones as zeros, but the receiver
still uses a threshold of 0.5 volts?
Do not try to determine σNOISE, but justify your answer.
If we change the probabilities of transmission but keep the same
digitization threshold, the various parts of the BER equation in (A)
are weighted differently (to reflect the different transmission
probabilities), but the total BER remains unchanged. This question is
essentially the same as one on PSet 3.
Problem .
Messages are transmitted along a noisy channel using the following
protocol: a "0" bit is transmitted as -0.5 Volt and a "1" bit as 0.5
Volt. The PDF of the total noise added by the channel, H, is shown
below. It is not a Gaussian.
Compute H(0), the maximum value of H.
The area under the PDF is 1, so (0.5)*H(0)*(1+0.5) = 1 from which we get H(0) = 4/3.
It is known that a "0" bits 3 times as likely to be transmitted
as a "1" bit. The PDF of the message signal, M, is shown below. Fill
in the values P and Q.
We know that Q=3P and that P+Q=1, so Q=0.75 and P=.25.
If the digitization threshold voltage is 0V, what is the bit error rate?
The plot below shown the PDF of the received voltage in magenta. For a threshold
voltage of 0, there is only one error possible: a transmitted "0" received as a
"1". This error is equal to the area of the triangle formed by the dotted black
line and the blue line = 0.5*0.5*0.5 = 0.125.
What digitization threshold voltage would minimize the bit error rate?
We'll minimize the bit error rate if the threshold voltage is chosen
at the voltage where the red and blue lines intersect.
By looking at the plot from the previous answer, let the ideal threshold
be x and the value of the
PDF at the intersection point be y. Then y/x=2/3 and y/(0.5-x)=1,
so x = 0.3V.
Problem .
Ben Bitdiddle studies the bipolar signaling scheme from 6.02 and decides to extend it to a 4-level signaling scheme, which he calls Ben's Aggressive Signaling Scheme, or BASS. In BASS, the transmitter can send four possible signal levels, or voltages: , where is some positive value. To transmit bits, the sender's mapper maps consecutive pairs of bits to a fixed voltage level that is held for some fixed interval of time, creating a symbol. For example, we might map bits “00” to , “01” to , “10” to , and “11” to . Each distinct pair of bits corresponds to a unique symbol. Call these symbols s_minus3, s_minus1, s_plus1, and s_plus3. Each symbol has the same prior probability of being transmitted.
The symbols are transmitted over a channel that has no distortion but does have additive noise, and are sampled at the receiver in the usual way. Assume the samples at the receiver are perturbed from their ideal noise-free values by a zero-mean additive white Gaussian noise (AWGN) process with noise intensity , where is the variance of the Gaussian noise on each sample. In the time slot associated with each symbol, the BASS receiver digitizes a selected voltage sample, r, and returns an estimate, s, of the transmitted symbol in that slot, using the following intuitive digitizing rule (written in Python syntax):
def digitize(r):
if r < -2A:
s = sminus3
elif r < 0:
s = sminus1
elif r < 2A:
s = splus1
else: s = splus3
return s
The power of a symbol transmission is defined as the square of the voltage level at which the symbol is transmitted. What is the average power level, , of a symbol transmission in BASS (i.e., the average power dissipated at the transmitter)?
Ben wants to calculate the symbol error rate for BASS, i.e., the probability that the symbol chosen by the receiver was different from the symbol transmitted. Note: we are not interested in the bit error rate here. Help Ben calculate the symbol error rate by answering the following questions below from 2 through 8
Suppose the sender transmits symbol s_plus3. What is the conditional symbol error rate given this information; i.e., what is P(symbolError| s_plus3 sent) ? Express your answer in terms of , , and the function, defined as
Using the Gaussian distribution for the noise: the desired probability is: Substituting , , and into the above equation, we obtain: Since the Gaussian distribution is symmetric, this becomes:
Now suppose the sender transmits symbol s_plus1. What is the conditional symbol error rate given this information, in terms of , , and the function?
The conditional symbol error rates for the other two symbols don't need to be calculated separately.
An error occurs if the received signal is less that or greater than . Therefore, where we used the results from problem 8.
The symbol error rate when the sender transmits symbol s_minus3 is the same as the symbol error rate of which of these symbols?
s_minus1.
s_plus1.
s_plus3.
s_plus3 because the Gaussian distribution is symmetric and the distance from the mean of s_minus3 to the error threshold is equal to the distance from the mean of s_plus3 to its error threshold.
The symbol error rate when the sender transmits symbol s_minus1 is the same as the symbol error rate of which of these symbols?
s_minus3.
s_plus1.
s_plus3.
s_plus1 by symmetry.
Now suppose the sender transmits symbol s_minus1. What is the conditional symbol error rate given this information, in terms of , , and the function? Do not recalculate; use symmetry and your previous answer(s).
TODO
Combining your answers to the questions above, what is the symbol error rate in terms of , , and the function? Recall that all symbols are equally likely to be transmitted.
The symbol error rate is:
Express the answer to the above question in terms of the signal-to-noise ratio. The average signal power is in the answer to one of the questions above.