JNTU B.Tech III DIGITAL COMMUNICATIONS Previous Question Paper (March - 2021 - R16)

Sampling is the process of converting a continuous-time signal into a discrete-time signal by taking samples at periodic intervals. According to the Nyquist-Shannon sampling theorem, a band-limited signal with maximum frequency f_m must be sampled at a rate f_s ≥ 2f_m to avoid aliasing.

The primary sampling techniques are:

1. Ideal Sampling (Impulse Sampling): The signal is multiplied by a train of unit impulse functions. This results in the spectrum of the original signal being replicated at multiples of the sampling frequency.
2. Natural Sampling: The signal is multiplied by a periodic pulse train of finite width. The tops of the pulses follow the shape of the original waveform, resulting in a signal that retains the continuous nature of the message during the pulse duration.
3. Flat-Top Sampling (Sample and Hold): The signal is sampled and the amplitude is held constant for the duration of the pulse. This is the most practical method as it simplifies downstream processing, though it introduces an aperture effect that causes high-frequency attenuation.

Adaptive Differential Pulse Code Modulation (ADPCM) improves upon standard DPCM by dynamically adjusting the step size of the quantizer and the prediction filter coefficients based on the statistical properties of the input signal. This allows the system to handle non-stationary signals more efficiently.

The block diagram consists of:

1. Predictor: Estimates the current sample value based on previous reconstructed samples.
2. Quantizer: Quantizes the difference (error) signal between the input and the prediction.
3. Adaptation Logic: Monitors the variance of the input signal or the error signal to adjust the step size of the quantizer and the coefficients of the predictor in real-time.
4. Reconstruction Loop: Adds the quantized error to the predicted value to reconstruct the signal, which is then fed back to the predictor to maintain synchronization between the transmitter and receiver.

By adapting the parameters, ADPCM achieves a higher signal-to-quantization-noise ratio (SQNR) compared to fixed DPCM, especially for speech signals where the amplitude varies significantly over time.