Effect of increasing the number of samples/quantization levels in A/D conversion What is the result of using more samples (i.e., higher sampling rate and/or more quantization levels) during the conversion process?

Introduction / Context:
Signal fidelity in digital acquisition depends on two independent axes: how often we sample in time (sampling rate) and how finely we quantize in amplitude (number of bits/levels). Increasing either or both improves representation but can increase data size and processing demands.

Given Data / Assumptions:

• Sampling rate relates to time resolution and aliasing margins.
• Quantization levels relate to amplitude resolution and quantization noise.
• System storage and bandwidth scale with data rate and bit depth.

Concept / Approach:

Higher sampling rates reduce time-domain distortion and relax anti-alias filter demands (oversampling), while more quantization levels (more bits) reduce quantization step size, lowering quantization noise and improving SNR. Both improvements typically require more bits stored or transmitted and more processing power.

Step-by-Step Solution:

Verification / Alternative check:

Quantitatively, ideal quantization SNR ≈ 6.02 * N + 1.76 dB; raising N increases SNR. Oversampling techniques improve effective resolution after noise shaping and digital filtering.

Why Other Options Are Wrong:

“More errors” is false; properly designed systems improve or maintain quality. Choosing only one factor (bits or accuracy) neglects the coupled reality that accuracy improvements often imply more bits or more samples.

Common Pitfalls:

Confusing sampling rate with quantization depth; assuming infinite improvement without considering analog front-end noise and jitter which impose practical limits.

Final Answer:

More bit requirements and more accurate signal representation