In this lecture, we are going to learn about the Adaptive Delta Modulation. In the previous lecture, we have seen the Delta modulation, but due to some disadvantages, we will learn why Adaptive Delta Modulation is used. So let’s start the discussion.
Reason to use Adaptive Delta Modulation
- To overcome the quantization errors due to slope overload and granular noise, the step size \Delta 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. Also, if the input is varying slowly, the step size is reduced. Then, this method is known as Adaptive Delta Modulation (ADM). The adaptive delta modulators can take continuous changes in step size or discrete changes in step size.
Adaptive Delta Modulation Block Diagram
1. Transmitter Part:
- Below figure (1) shows the transmitter and receiver of the adaptive delta modulator.
- The logic for step size control is added in the diagram. Step size increased or decreased according to a specified rule and example if one-bit quantizer output.
- As an example, if one-bit quantizer output is high (i.e. 1) then step size may be doubled for the next sample. If one-bit quantizer output is low, then step size may be reduced by one step.
- Figure (2) shows the staircase waveforms of the adaptive delta modulator and the sequence of bits to be transmitted.
2. Receiver Part:
- In the receiver of the adaptive delta modulator shown in figure (1), there are two portions. The first portion produces the step size from each incoming bit. Exactly the same process is followed as that in the transmitter. The previous input and present input decides the step size. It is then applied to an accumulator which builds up the staircase waveform. The low-pass filter then smoothens out the staircase waveform to reconstruct the original signal.
Advantages of Adaptive Delta Modulation
- Adaptive delta modulation has certain advantages over delta modulation as:
- the signal to noise ratio becomes better than ordinary delta modulation because of the reduction in slope overload distortion and idle noise.
- because of the variable step size, the dynamic range of ADM is wider than simple DM.
- utilization of bandwidth is better than delta modulation.
Comparison Between the Delta Modulation and Adaptive Delta Modulation
|Sr.No||Parameters of comparison||Delta Modulation (DM)||Adaptive Delta Modulation (ADM)|
|1.||A number of bits.||It uses only one bit for one sample.||Only one bit is used to encode one sample.|
|2.||Levels and step size||Step size is kept fixed and cannot be varied.||According to the signal variation, step size varies.|
|3.||Quantization error and distortion||Slope overload distortion and granular noise are present.||Quantization noise is present but other errors are absent.|
|4.||Transmission bandwidth||The lowest bandwidth is required.||The lowest bandwidth is required.|
|5.||Feedback||Feedback exists in the transmitter.||Feedback exists.|
|6.||Complexity of implementation||Simple||simple|
Frequently Asked Questions on Adaptive Delta Modulation
What is Adaptive delta modulation?
Answer: An Adaptive Delta Modulator is basically used to quantize the difference between the current signal value and the predicted value of the following signal. It uses variable step height in order to predict the consequent values.
What is the difference between delta modulation and Adaptive delta modulation?
Answer: DM stands for delta modulation while ADM stands for adaptive delta modulations. Both are used for only one bit per one sample, In Delta, the modulation step size is kept fixed and cannot be varied while in Adaptive delta modulation according to signal variations step sizes varies.
What are the advantages of ADM?
Answer: Adaptive delta modulation decreases slope error present in delta modulation. During demodulation, it uses a low pass filter which removes the quantized noise. The slope overload error and granular error present in delta modulation are solved using this modulation.
How do you reduce the slope error in ADM?
Answer: ADM reduces slope error, at the expense of increasing quantizing error. This error can be reduced by using a low-pass filter.