SLAAEO8 October 2024 MSPM0C1103 , MSPM0C1103-Q1 , MSPM0C1104 , MSPM0C1104-Q1 , MSPM0G1105 , MSPM0G1106 , MSPM0G1107 , MSPM0G1505 , MSPM0G1506 , MSPM0G1507 , MSPM0G1519 , MSPM0G3105 , MSPM0G3105-Q1 , MSPM0G3106 , MSPM0G3106-Q1 , MSPM0G3107 , MSPM0G3107-Q1 , MSPM0G3505 , MSPM0G3505-Q1 , MSPM0G3506 , MSPM0G3506-Q1 , MSPM0G3507 , MSPM0G3507-Q1 , MSPM0G3519 , MSPM0L1105
The Nyquist theorem states that in order to reconstruct an analog input signal, the signal must be sampled at a sampling frequency fS greater than twice the maximum frequency component of the input signal. Not complying with Nyquist's theorem can lead to frequency aliasing effects, and analog signals cannot be completely reconstructed from input samples. Therefore, for most applications, a low-pass filter is required at the ADC input to filter frequencies below half the sampling frequency. And oversampling samples the input analog signal at a rate higher than the Nyquist frequency limit, and reduces the sampling rate through extraction after sampling.
Assuming that quantization noise is superimposed on the signal in the form of white noise, its power density is uniformly distributed within the Nyquist frequency limit, and this power density is independent of the sampling frequency. When sampling at a rate higher than the Nyquist frequency limit, due to the constant quantization noise power and increased sampling bandwidth, the noise power falling within the signal bandwidth is greatly attenuated, and the signal-to-noise ratio and effective bit of the ADC are improved.