ECE 4371, Fall, 2013 Introduction to Telecommunication Engineering/Telecommunication Laboratory

1. ECE 4371, Fall, 2013Introduction to Telecommunication Engineering/Telecommunication Laboratory Zhu Han Department of Electrical and Computer Engineering Class 8 Sep. 23th, 2013

2. Outline • PAM/PWM/PPM/PCM • TDM • Quantization • SNR vs. quantization level • Two optimal rules • VQ quantization • A law/u law • Practical ADC/DAC

3. PAM, PWM, PPM, PCM

4. k1 k2 k3 k4 k5 k6 Frequency c f Time t Time Division Multiplexing • Entire spectrum is allocated for a channel (user) for a limited time. • The user must not transmit until its next turn. • Used in 2nd generation • Advantages: • Only one carrier in the medium at any given time • High throughput even for many users • Common TX component design, only one power amplifier • Flexible allocation of resources (multiple time slots).

5. Time Division Multiplexing • Disadvantages • Synchronization • Requires terminal to support a much higher data rate than the user information rate therefore possible problems with intersymbol-interference. • Application: GSM • GSM handsets transmit data at a rate of 270 kbit/s in a 200 kHz channel using GMSK modulation. • Each frequency channel is assigned 8 users, each having a basic data rate of around 13 kbit/s

6. Quantization • Scalar Quantizer Block Diagram • Mid-tread • Mid-rise

7. Equations

8. Quantization Noise

9. Quantization Noise Level

10. Quantization SNR , 6dB per bit

11. Example • SNR for varying number of representation levels for sinusoidal modulation 1.8+6 X dB

12. Conditions for Optimality of Scalar Quantizers Let m(t) be a message signal drawn from a stationary process M(t) -A m  A m1= -A mL+1=A mk mk+1 for k=1,2,…., L The kth partition cell is defined as Jk: mk< m  mk+1 for k=1,2,…., L d(m,vk): distortion measure for using vk to represent values inside Jk.

13. Condition for Optimal Quantizer

14. Condition One

15. Condition Two

16. Vector Quantization image and voice compression, voice recognition statistical pattern recognition volume rendering

17. Distortion SQ VQ Rate (bps) Rate Distortion Curve • Rate: How many codewords (bits) are used? • Example: 16-bit audio vs. 8-bit PCM speech • Distortion: How much distortion is introduced? • Example: mean absolute difference(L1), mean square error (L2) • Vector Quantizer often performs better than Scalar Quantizer with the cost of complexity

18. Non-uniform Quantization • Motivation • Speech signals have the characteristic that small-amplitude samples occur more frequently than large-amplitude ones • Human auditory system exhibits a logarithmic sensitivity • More sensitive at small-amplitude range (e.g., 0 might sound different from 0.1) • Less sensitive at large-amplitude range (e.g., 0.7 might not sound different much from 0.8) histogram of typical speech signals

19. ^ ^ x y Non-uniform Quantizer F: nonlinear compressing function F-1: nonlinear expanding function F and F-1: nonlinear compander y Q F F-1 x Example F: y=log(x) F-1: x=exp(x) We will study nonuniform quantization by PCM example next A law and  law EE 541/451 Fall 2006

20.  Law/A Law • The -law algorithm (μ-law) is a companding algorithm, primarily used in the digitaltelecommunication systems of North America and Japan. Its purpose is to reduce the dynamic range of an audio signal. In the analog domain, this can increase the signal to noise ratio achieved during transmission, and in the digital domain, it can reduce the quantization error (hence increasing signal to quantization noise ratio). • A-law algorithm used in the rest of worlds. • A-law algorithm provides a slightly larger dynamic range than the mu-law at the cost of worse proportional distortion for small signals. By convention, A-law is used for an international connection if at least one country uses it.

21.  Law

22. A Law

23.  Law/A Law Example 6.2 and 6.3

24. Analog to Digital Converter • Main characteristics • Resolution and Dynamic range : how many bits • Conversion time and Bandwidth: sampling rate • Linearity • Integral • Differential • Different types • Successive approximation • Slope integration • Flash ADC • Sigma Delta

25. Successive approximation • Compare the signal with an n-bit DAC output • Change the code until • DAC output = ADC input • An n-bit conversion requires n steps • Requires a Start and an End signals • Typical conversion time • 1 to 50 ms • Typical resolution • 8 to 12 bits • Cost • 15 to 600 CHF

26. Vin - + Counting time StartConversion StartConversion Enable S Q R N-bit Output Counter C Clk Oscillator IN Single slope integration • Start to charge a capacitor at constant current • Count clock ticks during this time • Stop when the capacitor voltage reaches the input • Cannot reach high resolution • capacitor • comparator

27. Flash ADC • Direct measurement with 2n-1 comparators • Typical performance: • 4 to 10-12 bits • 15 to 300 MHz • High power • Half-Flash ADC • 2-step technique • 1st flash conversion with 1/2 the precision • Subtracted with a DAC • New flash conversion • Waveform digitizing applications

28. Sigma-Delta ADC

29. Over-sampling ADC • Hence it is possible to increase the resolution by increasing the sampling frequency and filtering • Reason is the noise level reduce by over sampling. • Example : • an 8-bit ADC becomes a 9-bit ADC with an over-sampling factor of 4 • But the 8-bit ADC must meet the linearity requirement of a 9-bit

30. Resolution/Throughput Rate

31. Digital to Analog Converter • Pulse Width Modulator DAC • Delta-Sigma DAC • Binary Weighted DAC • R-2R Ladder DAC • Thermometer coded DAC • Segmented DAC • Hybrid DAC