Sampling a Continuous-Time Signal

Spectrum of a sampled signal with aliases shown

The signals of our interest — wireless communication waveforms — are continuous-time as they have to travel through a real wireless channel. To process such a signal using digital signal processing techniques, the signal must be converted into a sequence of numbers. This can be done through the process of periodic sampling.

A continuous-time signal in time and frequency domains

Consider a band-limited continuous-time signal s(t) and its frequency domain representation S(F) with bandwidth B, shown in the above Figure. A discrete-time signal s[n] can be obtained by taking samples of s(t) at equal intervals of T_S seconds. This process is shown in Figure below, and mathematically represented as

    \begin{equation*}       s[n] = s(t)\bigg| _{t=nT_S} \quad -\infty < n < \infty     \end{equation*}

A continuous-time signal sampled in time domain

The time interval T_S seconds between two successive samples is called the sampling period or sample interval, and its reciprocal 1/T_S = F_S is called the sample rate or sampling frequency. Sample rate F_S is the most fundamental parameter encountered in digital signal processing applications.

Let us find out what happens in frequency domain as a result of this process. Consider a continuous-time sinusoidal signal

    \begin{equation*}       s(t) = A \cos (2\pi Ft + \theta)     \end{equation*}

and obtain its sampled version at a rate F_S = 1/T_S samples/second.

(1)   \begin{align*}       s[n] &= s(t)| _{t=nT_S} \nonumber \\            &= A \cos \left(2\pi F nT_S + \theta\right) \nonumber \\            &= A \cos \left(2\pi F \frac{n}{F_S} + \theta\right) \nonumber \\            &= A \cos \left(2 \pi \frac{F}{F_S} n + \theta\right)      \end{align*}

Note that F/F_S above is the frequency of a discrete-time sinusoid s[n]. Let us sample at the same rate F_S another sinusoid with continuous frequency F + k F_S, where k = \pm 1, \pm 2,\cdots.

    \begin{align*}       s(t) &= A \cos \left\{2 \pi (F +kF_S) t + \theta)\right\}\nonumber \\       s[n] &= A \cos \{2 \pi (F + kF_S) nT_S + \theta\} \nonumber\\            &= A \cos \left(2 \pi \frac{F + kF_S}{F_S} n + \theta \right)\nonumber \\            &= A \cos \left(2 \pi \frac{F}{F_S} n + 2\pi kn + \theta\right) \nonumber\\            &= A \cos \left(2 \pi \frac{F}{F_S} n + \theta\right)     \end{align*}

which is exactly the same as discrete-time sinusoid in Eq (1). It can be concluded that at the output of sampling process, it is impossible to distinguish between two discrete-time signals whose frequencies are F_S Hz apart. So for an arbitrary frequency F after sampling

    \begin{align*}     F &= F + 1F_S \\       &= F - 1F_S \\       &= F + 2F_S \\       &= F - 2F_S \\     \end{align*}

and so on. It is just like saying that any two angles 360 ^\circ apart are all the same. For example,

    \begin{align*}     30 ^\circ &= 30^\circ + 1(360^\circ) = 390 ^\circ \\               &= 30^\circ - 1(360^\circ) = -330 ^\circ \\               &= 30^\circ + 2(360^\circ) = 750 ^\circ \\               &= 30^\circ - 2(360^\circ) = -690 ^\circ     \end{align*}

Therefore, all the following frequency ranges are the same:

    \begin{align*}     &\cdots\cdot \\     -2.5F_S \quad &\rightarrow \quad -1.5F_S \\     -1.5F_S \quad &\rightarrow \quad -0.5F_S     \end{align*}

    \begin{equation*} -0.5F_S \quad  \rightarrow \quad +0.5F_S}     \end{equation*}

    \begin{align*}     0.5F_S \quad &\rightarrow \quad 1.5F_S \\     1.5F_S \quad &\rightarrow \quad 2.5F_S \\     &\cdots\cdot     \end{align*}

The range -0.5F_S \rightarrow +0.5F_S is called the primary zone. The spectrum of the continuous-time signal s(t) shown in this Figure is now drawn in Figure below. We adopt the convention of indicating this zone within dotted red lines and drawing its spectral contents with solid lines while the spectral replicas with dashed lines.

Spectrum of a sampled signal with aliases shown

The fact that a continuous frequency higher than 0.5F_S Hz appears similar to a frequency F_S Hz apart from itself can be understood in time domain from Figure below. Observe that samples are taken at a rate such that both sinusoids pass through the same points. In fact, there are infinitely many sinusoids (F_S Hz apart) which pass through the same points, and hence become indistinguishable from each other after sampling.

Owing to the chosen sampling interval, a sinusoid and its next alias passing through the same points

In the light of above discussion, it is evident from this Figure that if a continuous-time signal has a bandwidth B greater than 0.5F_S, it will appear as an alias of a lower frequency within the range -0.5F_S \le F < +0.5F_S and distort the signal. This is illustrated in Figure below for a signal whose bandwidth B extends beyond the primary zone.

Spectral replicas overlapping with the signal spectrum and distorting the signal

Therefore, for a signal with bandwidth B, the sampling frequency should be such that the following inequality is satisfied to prevent any distortion in the sampled signal:

    \begin{equation*}       0.5F_S > B \nonumber     \end{equation*}

or written in another form

(2)   \begin{equation*} F_S > 2 B}     \end{equation*}

Sampling theorem


As shown above, sampling in time domain at intervals of T_S creates periodicity in frequency domain with a period of F_S = 1/T_S. Therefore, a band-limited continuous-time signal with highest frequency (or bandwidth) B Hz can be uniquely recovered from its samples provided that the sample rate F_S \ge 2B samples/second.

The frequency 2B is called the Nyquist rate while 0.5F_S is called the Nyquist frequency or folding frequency (see this Figure). Sampling theorem is one of the two most fundamental relations in digital signal processing, the other being the relationship between continuous and discrete frequencies.

A natural extension is to understand the notion of time in a discrete-time setting. As long as the sampling interval or sample rate is known, one can easily determine the period or frequency of a signal. For the sinusoid of Figure below for example, the period T is clearly 10 samples, and to find it in actual seconds, sample interval T_S or sample rate F_S must be known.

A discrete-time sinusoid sampled at regular intervals of T_S

For T_S = 0.1 seconds,

    \begin{align*}         T &= 10 ~\frac{\textmd{samples}}{\textmd{period}}~ \cdot ~ 0.1 ~\frac{\textmd{seconds}}{\textmd{sample}} = 1~ \textmd{second}     \end{align*}

and its frequency F = 1/T = 1 Hz. However, for a T_S = 0.002 seconds, the same discrete-time sinusoid has

    \begin{align*}         T &= 10 ~\frac{\textmd{samples}}{\textmd{period}}~ \cdot ~0.002 ~\frac{\textmd{seconds}}{\textmd{sample}} = 0.02~ \textmd{seconds}     \end{align*}

with frequency F = 1/T = 50 Hz. Interestingly, the samples of both sinusoids will be stored in memory as a sequence of numbers with no difference in discrete domain.

Is aliasing always harmful?


Aliasing – the reemergence of frequencies higher than 0.5F_S within the range -0.5F_S \le F < +0.5F_S of primary zone – is a consequence of disobeying the sampling theorem. It may seem so but aliasing is not always bad. In fact, there are three types of aliasing:

  1. Harmful aliasing that distorts the signal and must be avoided for proper representation of signal in discrete domain. This is when F_S < 2B.
  2. Useful aliasing that shifts the signal spectral bands up and down for free to our desired frequency through careful system design. This is employed in systems operating at multiple clock rates.
  3. Harmless aliasing that is neither good nor bad for the system. This occurs, for example, during band-limited pulse shape design to avoid inter-symbol interference (ISI).

Don’t worry much if it sounds too confusing at this stage. We will cover everything in detail when the topic arises.

A final remark about sampling a continuous-time signal is that for a fixed time interval of data collection, the more samples we take, the higher the energy in the resulting discrete-time signal is. This is because there will be more samples in the discrete-time signal during a fixed interval for a higher sampling rate, see its definition in energy and power.

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