Sampling Theorem

A bandlimited signal can be reconstructed exactly from its samples if the sampling rate exceeds twice its highest frequency (Nyquist rate); sampling too slowly causes aliasing, folding high frequencies into the baseband.

Key formulas & points

Skim these first — then read the full notes below.

  • Practical sampling: aperture time, hold, quantisation
  • Anti-aliasing low-pass before sampler
  • Zero-order hold introduces sin(x)/x distortion

Topic details

Introduction

The sampling theorem states that a signal bandlimited to f_max is fully determined by samples taken at f_s ≥ 2f_max. At exactly the Nyquist rate reconstruction is marginal; practice uses a comfortable margin.

Scope in B.Tech and GATE syllabus

If f_s < 2f_max, spectra overlap and high-frequency components appear as false low frequencies (aliasing), which cannot be removed after sampling. An anti-aliasing low-pass filter before the sampler bandlimits the input to prevent this.

Key relations & formulas

x(t)=Σx(nT)sinc(π(tnT)T)x(t) = Σ x(nT) sinc(\pi\frac{(t - nT)}{T})
(ideal reconstruction)
NyquistratefN=2fmaxNyquist rate f_{N} = 2 f_{max}
(bandlimited signal)

Formulas (Indian textbook notation)

  • Aliasing:frequenciesabovefs2foldintobasebandAliasing: frequencies above \frac{f_{s}}{2} fold into baseband

Notation and sign conventions

Relation 1 —
xx
x(t)=Σx(nT)sinc(π(tnT)T)x(t) = Σ x(nT) sinc(\pi\frac{(t - nT)}{T})
(ideal reconstruction)
Write this relation with symbols exactly as in Signals & Systems — Oppenheim & Willsky before substituting numbers. Examiners award partial marks for a correct setup even when arithmetic slips.
Relation 2 —
NyquistratefN=2fmaxNyquist rate f_{N} = 2 f_{max}
NyquistratefN=2fmaxNyquist rate f_{N} = 2 f_{max}
(bandlimited signal)
Write this relation with symbols exactly as in Signals & Systems — Oppenheim & Willsky before substituting numbers. Examiners award partial marks for a correct setup even when arithmetic slips.
Relation 3 —
Aliasing:frequenciesabovefs2foldintobasebandAliasing: frequencies above \frac{f_{s}}{2} fold into baseband

Formulas (Indian textbook notation)

  • Aliasing:frequenciesabovefs2foldintobasebandAliasing: frequencies above \frac{f_{s}}{2} fold into baseband
Write this relation with symbols exactly as in Signals & Systems — Oppenheim & Willsky before substituting numbers. Examiners award partial marks for a correct setup even when arithmetic slips.

Fundamentals and definitions

An alias frequency for a sinusoid f sampled at f_s appears at |f − k·f_s| within the baseband (0 to f_s/2). This is why a 30 Hz tone sampled at 50 Hz shows up as 20 Hz.

Governing relations in practice

Ideal reconstruction interpolates the samples with sinc functions; practical DACs use a zero-order hold that introduces a sin(x)/x amplitude roll-off, corrected by an equalising filter.

Design and analysis considerations

Oversampling (sampling well above Nyquist) relaxes the anti-aliasing filter and spreads quantisation noise over a wider band, improving effective resolution.

Assumptions and validity limits

State assumptions explicitly before using any relation for sampling theorem — steady state, uniform properties, linear elastic material, ideal gas, incompressible flow, etc., as applicable.
Wrong assumptions invalidate the entire solution even when the formula is correct. In Signals & Systems viva and GATE descriptive questions, listing valid assumptions often earns separate marks.

Step-by-step problem approach

1. Read the question and list given data with SI units (common in Signals & Systems papers).
2. Draw a neat labelled diagram where applicable — examiners in Indian universities award diagram marks even when arithmetic slips.
3. Identify which relation from this topic applies to sampling theorem.
4. Use equation 1:
xx
.
5. Use equation 2:
NyquistratefN=2fmaxNyquist rate f_{N} = 2 f_{max}
.
6. Substitute values, compute, and verify units and sign (direction).
7. State conclusion in one line — e.g. safe/unsafe, stable/unstable, feasible/infeasible.

Applications & exam relevance

Sampling Theorem appears in communications and control. In Indian electrical curricula this topic is tested because it connects theory to continuous and discrete signals.
GATE and semester exams often combine sampling theorem with earlier units — revise prerequisites before attempting mixed problems.
Industry interview panels sometimes ask: "Where did you use sampling theorem?" — answer with a lab, mini-project, or plant visit example if possible.

Common mistakes in exams

• Sampling at exactly f_max instead of 2f_max
• Believing aliasing can be filtered out after sampling (it cannot)
• Forgetting the anti-aliasing filter before the sampler
• Ignoring zero-order-hold sinc distortion in reconstruction

Quick revision checklist

Before attempting sampling theorem problems, confirm you can:
1. Practical sampling: aperture time, hold, quantisation
2. Anti-aliasing low-pass before sampler
3. Zero-order hold introduces sin(x)/x distortion
Revise the solved examples in Signals & Systems — Oppenheim & Willsky and one previous-year GATE or university paper for this unit.

Worked examples

Try the problem first — open the solution when you are ready to check.

Aliased frequency

Problem

A 1200 Hz sinusoid is sampled at 1000 Hz. Find the apparent (alias) frequency in the baseband.

Solution

Nyquist limit = f_s/2 = 500 Hz; 1200 Hz exceeds it, so aliasing occurs.
Alias = |f − k·f_s| within 0–500 Hz. With k = 1: |1200 − 1000| = 200 Hz.
200 Hz lies below 500 Hz, so the apparent frequency is 200 Hz.
A 1200 Hz tone is indistinguishable from 200 Hz at this sampling rate.

Conceptual check — Sampling Theorem

Problem

In a Signals & Systems semester or GATE paper you are asked: "State the main assumption, the governing relation, and one practical consequence of sampling theorem." What should a complete answer include?

Exams & GATE

Oppenheim — minimum sampling rate for given signal bandwidth.

📖 Standard books (India)

  • Signals & SystemsOppenheim & Willsky

    Read: Syllabus unit

    Laplace, Fourier, and LTI systems