Contents
What is oversampling and noise shaping?
Oversampling means that the sampling rate is increased to several times what is required just to avoid aliasing. Shortly, we will see why this helps improving the resolution. Noise shaping means that the quantization noise is moved away from the signal band that we are interested in.
How do you keep quantization noise minimum?
The process of oversampling to reduce ADC quantization noise is straightforward. An analog signal is digitized at an fs sample rate that is higher than the minimum rate needed to satisfy the Nyquist criterion (twice the input analog signal’s bandwidth) and then lowpass filtered.
Why oversampling is used in a D converter and write the Nyquist rate for oversampling?
An oversampling converter uses a noise-shaping modulator to reduce the in-band quantization noise to achieve a high degree of resolution. The oversampling ratio, called M, is a ratio of the clock frequency to the Nyquist frequency of the input signal.
Which system is prone to quantization noise?
Quantization noise occurs in PCM only. Its greatest disadvantage is its requirement for large bandwidth.
What is the purpose of oversampling in SNR?
The idea of oversampling is to spread the quantization noise out so that it is no longer swamping out the noise floor of your signal which should be the limiting factor to SNR, and to allow for appropriate filter design to eliminate noise out of band from folding in at a lower sampling rate.
What does oversampling of a signal do to a signal?
Oversampling is capable of improving resolution, reducing noise and can be helpful in avoiding aliasing and phase distortion by relaxing anti-aliasing filter performance requirements. A signal is said to be oversampled by a factor of N if it is sampled at N times the Nyquist rate.
Is the quantization noise the limiting factor in oversampling?
You don’t want the quantization noise to be the limiting factor on your final SNR because you can usually do something about it (oversample). The idea is to oversample so that the quantization noise is below the system noise, thus making the system noise the limiting factor as it should be.
When does oversampling and averaging will work?
When Oversampling and Averaging Will Work The effectiveness of oversampling and averaging depends on the characteristics of the dominant noise sources. The key requi rement is that the noise can be modeled as white noise.