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Spectrum of a bandlimited signal as a function of frequency

A bandlimited signal is a deterministic or stochastic signal whose Fourier transform or power spectral density is zero above a certain finite frequency. In other words, if the Fourier transform or power spectral density has finite support then the signal is said to be bandlimited.

A bandlimited signal can be fully reconstructed from its samples, provided that the sampling rate exceeds twice the maximum frequency in the bandlimited signal. This minimum sampling frequency is called the Nyquist rate. This result, usually attributed to Nyquist and Shannon, is known as the Nyquist–Shannon sampling theorem, or simply the sampling theorem.

An example of a simple deterministic bandlimited signal is a sinusoid of the form . If this signal is sampled at a rate so that we have the samples , for all integers n, we can recover completely from these samples. Similarly, sums of sinusoids with different frequencies and phases are also bandlimited.

The signal whose Fourier transform is shown in the figure is also bandlimited. Suppose is a signal whose Fourier transform is , the magnitude of which is shown in the figure. The highest frequency component in is . As a result, the Nyquist rate is

or twice the highest frequency component in the signal, as shown in the figure. According to the sampling theorem, it is possible to reconstruct completely and exactly using the samples

for integer and

as long as

The reconstruction of a signal from its samples can be accomplished using the Whittaker–Shannon interpolation formula.

A bandlimited signal cannot be also timelimited. More precisely, a function and its Fourier transform cannot both have finite support. This fact can be proved by using the sampling theorem.

Proof: Assume that a signal which has finite support in both domains exists, and sample it faster than the Nyquist frequency. This finite number of time-domain coefficients should define the entire signal. Equivalently, the entire spectrum of the bandlimited signal should be expressible in terms of the finite number of time-domain coefficients obtained from sampling the signal. Mathematically this is equivalent to requiring that a (trigonometric) polynomial can have infinitely many zeros in bounded intervals since the bandlimited signal must be zero on an interval beyond a critical frequency which has infinitely many points. However, it is well-known that polynomials do not have more zeros than their orders due to the fundamental theorem of algebra. This contradiction shows that our original assumption that a time-limited and bandlimited signal exists is incorrect.

One important consequence of this result is that it is impossible to generate a truly bandlimited signal in any real-world situation, because a bandlimited signal would require infinite time to transmit. All real-world signals are, by necessity, timelimited, which means that they cannot be bandlimited. Nevertheless, the concept of a bandlimited signal is a useful idealization for theoretical and analytical purposes. Furthermore, it is possible to approximate a bandlimited signal to any arbitrary level of accuracy desired.

A similar relationship between duration in time and bandwidth in frequency also forms the mathematical basis for the uncertainty principle in quantum mechanics. In that setting, the "width" of the time domain and frequency domain functions are evaluated with a variance-like measure. Quantitatively, the uncertainty principle imposes the following condition on any real waveform:

where

W_B is a (suitably chosen) measure of bandwidth (in hertz), and

T_D is a (suitably chosen) measure of time duration (in seconds).

William McC. Siebert (1986). Circuits, Signals, and Systems. Cambridge, MA: MIT Press. 

• Bandwidth
• Nyquist–Shannon sampling theorem
• Nyquist rate
• Nyquist frequency