# Sampling Theorem Notes. Recall: That a time sampled signal is like taking a snap shot or picture of signal periodically.

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1 Sampling Theorem We will show that a band limited signal can be reconstructed exactly from its discrete time samples. Recall: That a time sampled signal is like taking a snap shot or picture of signal periodically. Consider the signal as follows Figure 1: Sampled Signal Multiplying by a pulse train, which is periodic with period, we get the signal which is a sampled version of. We find that

2 Because is periodic we can represent it as a Fourier series Where From this we find that and thus that so Finally taking the Fourier transform yields { } The previous equation can be rewritten as { } Using the phase shift property of the Fourier transform we find that { } Where

3 Figure 2: Spectrum of a Sampled Signal The interesting result of this exercise is that we find the sampled signal has a spectrum that contains copies of the transform centered at every. To recover this signal in entirety we simply have to place a low-pass filter of bandwidth centered at. Notice that we could also place a band-pass filter of bandwidth around any to get a modulated version of the signal where the carrier would be. What we also need to take note of is that if is less than the signal spectrum will have the copies overlapping or aliasing each other. In order to have no overlap we must have or which is referred to as the Nyquist rate. For a look at an aliased spectrum take a look at Figure 3.

4 Figure 3: Aliasing Due to Under Sampling Example 1: Sampling Theorem Question: Given the following signal find the NYquist interval and the sampling rate. Answer: The bandwidth of this signal is 5 khz and therefore the sampling rate 10 khz. Signal Reconstruction We will now show that a signal can in fact be reconstructed in its entirety simply by a well-placed low-pass filter. For a band limited signal with bandwidth to be we find the transfer function of the filter for reconstruction Note: the addition of the scaling factor.

5 In the time domain we find the transfer function to be For arguments sake we will say that is equal to 1 so that Figure 4: Reconstructing Sampled Signal In Figure 4 we can see that at all Nyquist sampling instants ( ), except at. When is inputted to the transfer function of our filter each sample, being an impulse, generates a sinc function of height equal to the strength of the sample at that instant in time seen in Figure 4.c. So ( )

6 Recall: The integral of a delta function is We also find that ( ) ( ) What we showed above is that the reconstruction of the signal from is an interpolation formula which yields as a weight sum of all the samples. Practical Signal Reconstruction Let s face it there is no such thing as an ideal sampling function i.e. a delta function. In the real world we are limited to more practical signals for instance shown in Figure 5. Figure 5: Practical Sampling For practicality should be easy to generate. For let analyze the accuracy of the reconstructed signal. Let s say that We can rewrite as follows After some manipulation we find that

7 The Fourier transform yields This is an interesting result because what it says is that rather than having to walk through a barrage of mathematically equations to determine how to reconstruct, we can simply find a transfer function that will essentially undue the results of sampling a signal with a non-ideal sampling function in this case. We will refer to this transfer function as an equalizer. The end result of apply an equalizer is that afterwards the signal simply becomes. Recall: We showed earlier that can be extracted by a simple, well placed, low-pass filter. Example 2: Non-Ideal Sampling Let s consider a simple interpolating pulse generator that generates short (zero-order hold) pulses. We have For reconstruction we first have The transfer function of is As a result the equalizer must satisfy { ( ) ( )

8 L =Levels Sampling Theorem Notes Quantization Figure 6: Quantization What it means to quantize a signal is to limit the possible values that a signal can take on. For instants, a discrete signal can have any range of amplitudes at a discrete time interval. Well in order to represent such a signal digitally, let s say, we would have to have an infinite amount of bits to do this. Since no computer exist that can handle an infinite bit data sample we quantize the data to fit within a range that we are comfortable with and fits our requirements for the system.

9 Example 3: Quantization Sampling Theorem Notes We have a radio receiver that will never receive a signal greater than (p = peak refer to Figure 6). Well at this point we can say to ourselves we do not need to have a signal greater that so all signals will only take on values between. Our accuracy or resolution of the signal depends on L. For larger values of L we will have move voltage resolution. Let s say we later find out that our voltage accuracy is we can use an L value of 8 where If we decided to represent this signal digitally we could say we need or 3 bits and now any incoming signal will take on values between and in steps. Another way to say it is that we can only detect a change in the incoming signals voltage. For an 8 bit binary number we have Binary Decimal x0.25v V But if we quantize the signal doesn t that change the signal all together? It turns out that we will not change the signal per se, rather we will increase the noise level of the signal. Previously we demonstrated the interpolation formula If we consider the as to be the sample of the message signal the interpolation is as follows

10 And for the quantized signal let be the sample of the signal then The distortion component is then [ ] The signal is an undesired and acts as noise which leads to the term quantization noise. To calculate the power of the quantization noise we do the following [ ] Where denotes the mean square power. Since the a since function and are orthogonal ever where but at we find that From the orthogonality relationship it follows that Assuming that the error is equally likely be anywhere between ( quantization noise is ) we will find that the Since the power of the message signal

11 It is clear that the Signal to Quantization Noise Ratio (SQNR) is Quantization will be in addition to whatever other noise is present in the system. Example 4: Power of Quantized Signal Question: If an audio signal has average power of 0.1W and a peak voltage of 1V. What is the resulting SQNR if L = 50,000 levels. Answer: When you see average power think mean square power or unless otherwise noted. With this we have Where a the decibel value is calculated as ( ) Question: How many bits would be required to represent one sample from this audio signal? Answer: Well there is no such thing as bits so we must round up to 16 bits in order to represent the largest possible quantization of the signal. Some Applications of the Sampling Theorem Sampling theorem is important because it allows a continuous signal to be sampled and then transmitted as a discrete number rather than a continuous time signal. Processing a continuous signal is then equivalent to processing a discrete signal. Discrete values can be transmitted by multiple variations of a pulse train. Pulse Amplitude Modulation (PAM)

12 Pulse Width Modulation (PWM) Pulse Position Modulation (PPM) Pulse Code Modulation (PCM) Although the method of creating and representing a quantized signal via a manipulated pulse train may vary the data being transmitted does not. See the image below.

13

14 Time Division Multiplexing Figure 7: Time Division Multiplexing In Figure 7 shows various forms of time multiplexing. Figure 7.a: Bit interleaving Figure 7.b: Word or byte interleaving Figure 7.c: Interleaving a of channels with different bit rates Figure 7.d: Alternate scheme for channels with different bit rates. Time division multiplexing allows multiple channels or user to share the same high speed data link. This is an important concept especially in the world of wireless communications because bandwidth is a limited resource.

15 Example 5: Data Transmission and Time Multiplexing Question: A wireless communication provider would like to time divide voice data from as many users as possible in order to transmit it over a new wireless link that has a bandwidth of 20MHz. In order to minimize data errors, engineers decide that they must sample data on the link at 20% above the Nyquist rate. If we can only transmit 2bits/s/Hz (a) What is the maximum sampling rate that can be achieved for this new wireless channel? (b) How many bits/s can be transmitted on this channel? (c) If each user will be transmitting data at 500Kbs/s how many users can this channel serve? Answer: The Nyquist sampling criteria says that we must sample at. In this case we have a channel that has so, but we must also account for the 20% over sampling that engineering has advised us of thus which means that our max bandwidth is really only. Since we can only transmit 2bits/s/Hz we find the maximum bitrate of the channel is If each user transmits 500Kbs/s we find that the channel can service 8 users..

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