Digital Communications. A PCM signal is generated at the transmitter by carrying out three basic operations: sampling, quantizing, and encoding. In this lab experiment, the MATLAB software will be used to understand and simulate the basic concepts and mechanisms of uniform and nonuniform PCM quantizers. Sample Matlab Code Go through “quant_uniform. Artisteer 5.2.0.66471 Final With Keygen-rept By Senzati. m”. ©Yao Wang, 2006 EE3414:Quantization 26 Binary Encoding • Convert each.

Quantization & SQNR 1. Objective 1.1 To understand the process of quantization 1. Crypto Airdata 54 S2 Usb Drivers on this page. 2 To compare the signal to quantization noise ratio for different quantization levels. 1.3 Basics concept of modulation, channel introduction and demodulation using bpsk. Overview Division of the range of values of a wave into a finite number of sub ranges, each of which is represented by an assigned or quantized value within the sub range is called quantization. Quantization Quantization is the process of approximating a continuous range of values (or a very large set of possible discrete values) by a relatively-small set of discrete symbols or integer values. More specifically, a signal can be multi-dimensional and quantization need not be applied to all dimensions. Discrete signals (a common mathematical model) need not be quantized, which can be a point of confusion.

A common use of quantization is in the conversion of a discrete signal (a sampled continuous signal) into a digital signal by quantizing. Both of these steps (sampling and quantizing) are performed in analog-to-digital converters with the quantization level specified in bits. A specific example would be compact disc (CD) audio which is sampled at 44,100 Hz and quantized with 16 bits (2 bytes) which can be one of 65,536 (i.e. 216) possible values per sample.

Quantization Noise and SQNR Suppose that the signal to be quantized has a peak-to-peak value of 2 V [V] and that the number of bits in a quantization word is m. If there are 2 m quantization levels, then the quantization step size is given by m V 2 2=Δ The quantizer input is x[n] and the output is. The output can be expressed by the following equation: = x[n] + e[n] where e[n] is termed the quantization noise or quantization error. The noise sequence, e[n], is uncorrelated with the sequence x[n]. After quantization, actual information about the noise is forgotten. However, the statistics of [n]x̂ [n]x̂ Digital Communication Lab (EE TC4406) 2 docsity.com. The noise is known. Halloween Games For Teenagers more.

The noise is between −Δ/2 and Δ/2 and is uniformly distributed. The probability density function of the noise or error, e[n], is given by Fig. Below Figure: Probability density function of the quantization noise e[n] The SQNR is linearly proportional to the number of bits m in the ADC. For each extra bit of resolution in the ADC, there is improvement of 6 dB in the SQNR.Thus, the SQNR of the signal that is uniformly distributed between the negative and positive peak values becomes SQNRuniform = 6m [dB] == )2(log10)3(log10 210 2 10 nL 3. MATLAB Simulation 4.1 Sinusoidal Example In this simulation the student will write the function for quantization of sampled signal and then try to quantize different sampled signals using this function. Write the following m-script in new m-file: function [sqnr,a_quan,code]=u_pcm(a,n)%U_PCM Uniform PCM encoding of a sequence.% [SQNR,A_QUAN,CODE]=U_PCM(A,N)% a=input sequence.% n=number of quantization levels (even).% sqnr=output SQNR (in dB).% a_quan=quantized output before encoding.% code=the encoded output. Amax=max(abs(a)); a_quan=a/amax; b_quan=a_quan; Digital Communication Lab (EE TC4406) 3 docsity.com.