Delta Modulation & Adaptive Delta M.pptx

communication systems

1. UNIT 2 WAVEFORM CODING AND REPRESENTATION DM DPCM LPC LINE CODES

2. PCM PCM- Pulse Code Modulation A signal is pulse code modulated to convert its analog information into a binary sequence, i.e.,  1s  and  0s . The output of a PCM will resemble a binary sequence. The following figure shows an example of PCM output with respect to instantaneous values of a given sine wave.

3. PCM produces a series of numbers or digits, and hence this process is called as  digital . Each one of these digits, though in binary code, represent the approximate amplitude of the signal sample at that instant. In Pulse Code Modulation, the message signal is represented by a sequence of coded pulses. This message signal is achieved by representing the signal in discrete form in both time and amplitude.

4. PCM transmitter and receiver

5. Sampler: fs >=2W This is the technique which helps to collect the sample data at instantaneous values of message signal, so as to reconstruct the original signal. The sampling rate must be greater than twice the highest frequency component  W  of the message signal, in accordance with the sampling theorem.

6. Quantizer is a process of reducing the excessive bits and confining the data. The sampled output when given to Quantizer , reduces the redundant bits and compresses the value.

7. Encoder The digitization of analog signal is done by the encoder. It designates each quantized level by a binary code. The sampling done here is the sample-and-hold process. These three sections LPF,Sampler,and Quantizer  will act as an analog to digital converter. Encoding minimizes the bandwidth used. SIGNAL  BINARY DIGITS

8. ADVANTAGES The PCM (pulse code modulation) convenient for long distance communication. It has a higher transmitter efficiency. It has a higher noise immunity.

9. DISADVANTAGES The PCM (pulse code modulation) requires large bandwidth as compared to analog system. Encoding, decoding and quantizing circuit of PCM is very complex.

10. DELTA MODULATION AND ADAPTIVE DELTA MODULATION

11. DM-Principle It transmits only one bit per sample. The present sample value is compared with previous sample value.The input signal x(t) is approximated to step signal by delta modulator. The difference betweeninput signal and staircase approximated signal confined to two levels + δ and - δ . If the difference is positive then approximated signal is increased by one step i.e ‘ δ ’ and ‘1’ is transmitted. If the difference is negative then approximated signal is reduced by i.e ‘ δ ’ and ‘0’ is transmitted.

12. Delta Modulation The type of modulation, where the sampling rate is much higher and in which the stepsize after quantization is of a smaller value  Δ , such a modulation is termed as  delta modulation . Delta Modulation is a simplified form of DPCM technique, also viewed as  1-bit DPCM scheme . As the sampling interval is reduced, the signal correlation will be higher.

13. Following are some of the features of delta modulation. An over-sampled input is taken to make full use of the signal correlation. The quantization design is simple. The input sequence is much higher than the Nyquist rate. The quality is moderate. The design of the modulator and the demodulator is simple. The stair-case approximation of output waveform. The step-size is very small, i.e.,  Δ  delta. The bit rate can be decided by the user. This involves simpler implementation.

14. DM Transmitter

15. BLOCK DIAGRAM EXPLANATION The predictor circuit in DPCM is replaced by a simple delay circuit in DM. From the above diagram, we have the notations as x( nTs )  = over sampled input/input signal ep ( nTs )  = summer output and quantizer input eq ( nTs )  = quantizer output = v( nTs )v( nTs ) xˆ( nTs ) = output of delay circuit/reconstructed signal u( nTs )  = input of delay circuit

16. x^( nTs ) = the previous value of the delay circuit eq ( nTs )  = quantizer output = v( nTs ) Hence, u( nTs )=u([n−1]Ts)+v( nTs ) x^( nTs ) = u([n−1]Ts) CONDITION: x( nTs ) > x^( nTs ) = + δ = 1 x( nTs ) < x^( nTs ) = - δ = 0 Accumulator is used to provide latest reconstructed signal

17. Delta Demodulator/Receiver

18. DM receiver The DM signal is added with one bit delayed reconstructed signal. This is accumulator operation The reconstructed signal is then passed through low pass filter for smoothing. If the input signal is binary “1” then it adds + δ to the previous output. If the input signal is binary “0” then one step δ is subtracted from the delayed signal . Maximum quantization error in DM is ε max = I δ I

19. Advantages of DM Over DPCM 1-bit quantizer Very easy design of the modulator and the demodulator However, there exists some noise in DM. Slope Over load distortion (when  Δ  is small) Granular noise (when  Δ  is large)

20. DRAWBACKS Slope overload distortion  arises because of large dynamic range of the input signal or in other words under maximum  slope  of the signal or the rate of rise of input signal x(t) is so high, step size becomes small to follow the step of the input waveform. This condition is called  slope overload  and the resulting quantization error is called  slope overload distortion . Granular noise  is the manifestation of random signals when the variation of the input signal is smaller than the step size. It occurs when the step size is too large compared to small variations in input signal.

21. DM OUTPUT

22. ADM In digital modulation, we have come across certain problem of determining the step-size, which influences the quality of the output wave. A larger step-size is needed in the steep slope of modulating signal and a smaller stepsize is needed where the message has a small slope. The minute details get missed in the process. So, it would be better if we can control the adjustment of step-size, according to our requirement in order to obtain the sampling in a desired fashion. This is the concept of  Adaptive Delta Modulation .

23. ADM To overcome the quantization errors due to slope overload and granular noise, the step size (δ) is made adaptive to variations in the input signal x(t). Particularly in the steep segment of the signal x(t), the step size is increased. When the input is varying slowly, the step size is reduced. Then the method is called Adaptive Delta Modulation (ADM).

24. Fig (a) shows the transmitter and fig (b) shows receiver of adaptive delta modulator. The logic for step size control is added in the diagram. The step size increases or decreases according to certain rule depending on one-bit quantizer output. For example if one-bit quantizer output is high (1), then step size may be doubled for next sample. If one-bit quantizer output is low, then step size may be reduced by one step. Fig shows the waveforms of adaptive delta modulator and sequence of bits transmitted.

25. In the receiver of adaptive delta modulator shown in Fig (b) the first part generates the step size from each incoming bit. Exactly the same process is followed as that in transmitter. The previous input and present input decides the step size. It is then given to an accumulator which builds up staircase waveform . The low-pass filter then smoothens out the staircase waveform to reconstruct the smooth signal.

26. ADM OUTPUT

27. Continuously variable slope delta modulation (CVSD) In ADM, the step size changes in discrete steps. When the step size varies continuously, then it is called continuously variable slope delta modulation (CVSD).

28. Advantages of Adaptive Delta Modulation The signal to noise ratio is better than ordinary delta modulation because of the reduction in slope overload distortion and granular noise. Because of the variable step size, the dynamic range of ADM is wide. Utilization of bandwidth is better than delta modulation

29. DPCM-Principle The differential pulse code modulation works on the principle of prediction. The value of the present sample is predicted from the past samples. The prediction may not be exact but it is very close to the actual sample value.

30. The sampled signal is denoted by x( nT s ) and predicted signal is denoted by xˆ( nT s ). The comparator finds out the difference between the actual sample value x( nT s ) and predicted sample value xˆ( nT s ). This is known as prediction error and it is denoted by e( nT s ). It can be defined as , e( nT s ) = x( nT s ) – xˆ( nT s )……………………….(1) The predicted value is produced by using a prediction filter.

31. The quantizer output signal gap e q ( nT s ) and previous prediction is added and given as input to the prediction filter.This signal is called   x q ( nT s ). This makes the prediction more and more close to the actual sampled signal.We can observe that the quantized error signal  e q ( nT s ) is very small and can be encoded by using small number of bits. Thus number of bits per sample are reduced in DPCM. The quantizer output can be written as , e q ( nT s ) =  e( nT s ) + q( nT s )………………………..(2) Here, q( nT s ) is the quantization error.

32. As shown in fig.2, the prediction filter input  x q ( nT s ) is obtained by sum xˆ( nT s ) and quantizer output. i.e., x q ( nT s ) = xˆ( nT s ) +   e q ( nT s )……………………..(3) Substituting the value of   e q ( nT s ) from eq.(2)  in the above eq. (3) , we get, x q ( nT s ) = xˆ( nT s ) +  e( nT s ) + q( nT s ) ………………….(4) eq.(1) is written as, e( nT s ) = x( nT s ) – xˆ( nT s ) e( nT s )  +  xˆ( nT s )  =  x( nT s ) Therefore, substituing the value of   e( nT s )  +  xˆ( nT s ) from the above equation into eq. (4), we get, x q ( nT s ) = x( nT s ) + q( nT s ) …………………..(5)

33. The decoder first reconstructs the quantized error signal from incoming binary signal. The prediction filter output and quantized error signals are summed up to give the quantized version of the original signal. Thus the signal at the receiver differs from actual signal by quantization error q( nT s ), which is introduced permanently in the reconstructed signal.

34. Advantages of DPCM As the difference between x( nT s ) and xˆ( nT s ) is being encoded and transmitted by the DPCM technique, a small difference voltage is to be quantized and encoded. This will require less number of quantization levels and hence less number of bits to represent them. Thus signaling rate and bandwidth of a DPCM  system will be less than that of PCM.

35. LINEAR PREDICTIVE CODING   LPC is a tool which represents digital speech signals in linear predictive model. This is mostly used in audio signal processing, speech synthesis, speech recognition, etc. Linear prediction is based on the idea that the current sample is based on the linear combination of past samples. The analysis estimates the values of a discrete-time signal as a linear function of the previous samples.

36. Speech model used in voice coders

37. There are two frequency sources One frequency source is used to generate unvoiced sound. Such sound are generated when the speaker pronounces letter such as s,f . A noise source is used to generate such unvoiced sounds. The voice sound are simulaed by impulse generator.The frequency of this generator is varied depending upon the pitch of the sound. These voiced or unvoiced passes through the filter. This filter represent vocal tract. In vocal tract this operation is done with the help of tongue,teeth,lips etc.

38. LPC Transmitter

39. LPC Transmitter The analyzer determines LP coefficients for the synthesis filter. Based on the LP coefficient the synthesis filter reconstruct the speech signal. The reconstructed signal and the original signal are compared and error is obtained. The LP coefficients and the error signal is multiplexed and transmitted.This signal is called LPC signal

40. LPC RECEIVER

41. The received LPC signal is applied to demultiplexer or decoder.It seperates filter parameters and error signal. The LP coefficients are given to the synthesis filter. The output of synthesis filter is added to error signal which gives speech signal . The analyzer is a digital filter.

42. Comparison – PCM Vs DM Vs ADM Vs DPCM

43. Line Coding Properties: Less transmission bandwidth Less Power Efficiency Easy error detection and correction Power Spectral Density Adequate timing content Transparency

44. Properties of line coding As the coding is done to make more bits transmit on a single signal, the bandwidth used is much reduced. For a given bandwidth, the power is efficiently used. The probability of error is much reduced. Error detection is done and the bipolar too has a correction capability. Power density is much favorable . The timing content is adequate. Long strings of  1s  and  0s  is avoided to maintain transparency.

45. TYPES There are 3 types of Line Coding Unipolar Polar Bi-polar SIGNAL Representation NRZ- Not Return to ZERO RZ – Return to ZERO

46. UNIPOLAR NRZ

47. UNIPOLAR RZ

48. POLAR NRZ

49. POLAR RZ

50. BIPOLAR NRZ & RZ