# Understanding White Noise in Signal-to-Noise Ratio: Calculating RMS Noise Value

Apr 5, 2024

When working with audio, video, or other digital systems, a key concern is often the presence of noise interfering with the desired signal. One common type of noise encountered in such systems is white noise, which is a random and equal-intensity distribution of all frequencies. The presence of this noise can impact performance, and in such situations, it is crucial to assess the level of interference – or, in technical terms, the signal-to-noise ratio (SNR). In this article, we will help you understand how to calculate the root-mean-square (RMS) of noise in a system where the SNR is given in decibels (dB).

First, let us explain what SNR is and how it is measured. SNR is simply the ratio of the power of the desired signal to the power of the noise. This value is often expressed in decibels (dB) to make it more easily interpretable. A higher SNR is indicative of a lower noise level, with an SNR of 12 dB, for example, meaning that the power of the signal is 12 dB more than that of the noise.

To calculate the RMS value of noise from the given SNR, it is crucial to determine the linear SNR value by converting from dB. Here's the formula:

SNR (linear) = 10 ^(SNR_dB/10)

In our example, the SNR is given as 12 dB. Therefore, we can calculate:

SNR (linear) = 10 ^ (12/10) = 15.8489

The next step involves using the known RMS value of the signal (V_signal) to calculate the RMS noise value (V_noise). Using the formula:

V_noise = V_signal / sqrt(SNR (linear))

Assuming the RMS value of the signal to be 1V:

V_noise = 1 / sqrt(15.8489) = 1 / 3.9811 = 0.2512 V

In this example, the RMS value of the white noise in the system is approximately 0.2512 volts.

In conclusion, understanding the Signal-to-Noise Ratio and calculating the RMS noise value can be extremely helpful when working with digital systems that may include interference from white noise. These calculations can provide insight into potential improvements to system performance by reducing the presence of noise interfering with the signal.