What is Quantization & Quantizer Saturation || Analog to Digital Conversion || ADC

-------------------- Credit : Muhammad Tilal -------------------------- ----------------------____________________________----------------------------- link next: ----------------https://youtu.be/RahAiMiLPwg------------------------ concepts all about communication • Quantization can be referred to as‘rounding off’ which approximates/rounds off an input value to a predefined level. • Due to this rounding off, some useful information is lost which in turn causes noise/distortion in the output signal. This noise is know as ‘Quantization Noise’. • Quantization noise is inversely proportional to the number of quantization levels. • For uniform quantization, quantization error (e) is uniformly distributed and can be represented as a uniform probability density function. • Also, the above mentioned quantization noise is considered to be zero mean. For a zero mean signal, the average power equal to its variance, σ Quantization Digital systems can only represent sample amplitudes with a finite set of prescribed values, and thus it is necessary for A/D converters to quantize the values of the samples x[n]. A typical form of quantization uses uniform quantization steps, where the input voltage is either rounded or truncated. Two forms of quantization error e[n] exist: 1. Quantization noise: due to rounding or truncation over the range of quantizer outputs; and 2. Saturation (“peak clipping”): due to the input exceeding the maximum or minimum quantizer output. Both types of error are illustrated for the case of a sinusoidal signal in the figure on the next slide. Quantization noise can be minimized by choosing a sufficiently small quantization step. We will derive a method for quantifying how small is “sufficient”. Saturation can be avoided by carefully matching the fullscale range of an A/D converter to anticipated input signal amplitude ranges. For rounding uniform quantizers, the amplitude of the quantization noise is in the range For small ∆ it is reasonable to assume that e[n] is a random variable uniformly distributed over For fairly complicated signals, it is reasonable to assume that successive quantization noise values are uncorrelated and that e[n] is uncorrelated with x[n]. Thus, e[n] is assumed to be a uniformly distributed white noise sequence with a mean of zero and variance: For a B+1)-bit quantizer with full-scale Xm, the noise variance (or power) is:Advantages: no coding delay not signal specific Disadvantages: high bit rates e.g. Wireless telephony requires 11 bits for “toll quality” analog telephone quality. If fs = 10,000 Hz ⇒ bit rate = 110,000 bps, which may be impractical for wireless systems. Howev er, consider a CD player that uses 16-bit PCM ⇒ SNR ≈ 88.75 dB & bit rate ≈ 320,000 bps, which is acceptable for wired applications. Uniform quantization is suboptimal for many applications. Consider the probability density function (p.d.f.) of speech: , all about electronics communication , block diagram of the communication system , analog and digital communication , analog and digital communication all about electronics , communication all about electronics , elements of communication system , block diagram of communication system , introduction to analog and digital communication , communication engineering , digital communication , communication system , analog communication , modulation Bernard Sklar, Digital Communications- Fundamentals and Applications, 2 nd Edition. [2] Louis E. Frenzel Jr., Principles of Electronic Communication Systems, 4 th Edition McGraw Hill Education, ISBN: 978—0-07-337385-0

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