# Noise & Interference

Created: April 2020Last Upated: 07/06/2023

Introduction

Noise is a pervasive factor that can impact the stability and performance of electronic systems. It is important to distinguish between interference and noise, with interference being artificial noise and noise being either natural or man-made. Understanding the basics of noise sources, their impact, and their correlation with external factors is crucial for effectively debugging and identifying the root causes of issues. In this article, we will explore common hardware system design and testing issues related to noise and interference.

Common Electronic Noise

Interference

## Differential Mode Mode Noise

The red-shaded region highlights the Differential Mode Noise affecting the transmit signal.

## Inter Symbol Interference

Note: For the example, we spaced out each bits to visually demonstrate the smearing effect into next symbol window. However in practice, the symbols are peseorandomly generated (i.e, 11011000. )

Quantifying Noise & Interference levels Using Power Spectral Density (PSD) Estimation

In a practical setting, time-series data often serves as the initial point of analysis, offering a preliminary view of how noise and interference impact signal quality within an electronic system's communication network. To delve deeper into quantifying these effects, spectral estimation techniques based on time-series data are commonly employed. One widely-used method for this purpose is Welch's method for Power Spectral Density (PSD) estimation.

In essence, Welch's method allows us to examine the power of the signal in the frequency domain, providing a more comprehensive understanding of noise characteristics. Analyzing the signal in the Fourier domain is particularly useful because it helps isolate individual frequency components, making it easier to identify and mitigate sources of noise and interference.

An illustration demonstrating the impact of added noise levels on spectral esitimation is provided below:

## PSD Estimation

The Signal-to-Noise Ratio (SNR) is a measure that quantifies how much a signal has been corrupted by noise. It is usually defined as the ratio of the power of a signal to the power of background noise. Here are some common ways to calculate i

Noise Mitigation

One we identify the noise and inteference source and qualify it’s impact on signal quality. We can implement following migtigations in noise suppresion.

Summary and Conclusion

Thermal, pink, and quantization noise are the three dominant types of noise in electronics. Reducing the operating temperature of a product helps reduce the impact of dominant analog noise. Quantization noise limits the resolution of analog-to-digital conversion. Both analog and digital noise can significantly affect the performance of electronic systems. Understanding the sources and impact of noise is crucial for implementing the appropriate noise mitigation solutions in hardware system design.