How does oversampling improve ADC resolution?
Oversampling Description As a general guideline, oversampling the ADC by a factor of four provides one additional bit of resolution, or a 6 dB increase in dynamic range. Increasing the oversampling ratio (OSR) results in overall reduced noise and the DR improvement due to oversampling is ΔDR = 10log10 (OSR) in dB.
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.
Why Gaussian noise is additive?
Additive white Gaussian noise (AWGN) is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature. Additive because it is added to any noise that might be intrinsic to the information system.
Why do we use Gaussian noise?
The reason why a Gaussian makes sense is because noise is often the result of summing a large number of different and independent factors, which allows us to apply an important result from probability and statistics, called the cen tral limit theorem.
How do I get rid of Gaussian noise?
Removing Gaussian noise involves smoothing the inside distinct region of an image. For this classical linear filters such as the Gaussian filter reduces noise efficiently but blur the edges significantly.
When to use additive Gaussian noise in octave?
Matlab/Octave communication toolbox has an inbuilt function named – awgn () with which one can add an Additive Gaussian White Noise to obtain the desired Signal-to-Noise Ratio (SNR). The main usage of this function is to add AWGN to a clean signal (infinite SNR) in order to get a resultant signal with a given SNR (usually specified in dB).
How to calculate the oversampling factor for AWGN?
It also returns the noise vector ‘n’ that is added to the signal ‘s’ and the power spectral density N0 of noise added Parameters: s : input/transmitted signal vector SNRdB : desired signal to noise ratio (expressed in dB) for the received signal L : oversampling factor (applicable for waveform simulation) default L = 1.
How to add white Gaussian noise to a signal?
The function y=awgn(x,SNR,’measured’), first measures the power of the signal vector and then adds white Gaussian Noise to for the given SNR level in dB. The resulting signal y is guaranteed to have the specified SNR.
How is AWGN noise vector used to generate Snr?
The function adds AWGN noise vector to signal ‘s’ to generate a resulting signal vector ‘r’ of specified SNR in dB.