1 / 21

Fundamentals of Digital Communication

Fundamentals of Digital Communication. Digital communication system. Input Signal Analog/ Digital. Low Pass Filter. Sampler. Quantizer. Source Encoder. Channel Encoder. Multiplexer. Carrier. Modulator. Pulse Shaping Filters. Line Encoder. To Channel. De- Modulator.

Download Presentation

Fundamentals of Digital Communication

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Fundamentals of Digital Communication

  2. Digital communication system Input Signal Analog/ Digital Low Pass Filter Sampler Quantizer Source Encoder Channel Encoder Multiplexer Carrier Modulator Pulse Shaping Filters Line Encoder To Channel De- Modulator Receiver Filter Detector From Channel Carrier Ref. Signal at the user end Digital-to-Analog Converter Channel Decoder De- Multiplexer

  3. Sampling Time domain Frequency domain

  4. LP filter Nyquist rate aliasing Aliasing effect

  5. Sampling process Analog signal Pulse amplitude modulated (PAM) signal Sampling theorem • Sampling theorem: A bandlimited signal with no spectral components beyond , can be uniquely determined by values sampled at uniform intervals of • The sampling rate, is called Nyquist rate.

  6. Average quantization noise power • Signal peak power • Signal power to average quantization noise power Out Quantized values In Quantization • Amplitude quantizing: Mapping samples of a continuous amplitude waveform to a finite set of amplitudes.

  7. Encoding (PCM) • Pulse code modulation (PCM): Encoding the quantized signals into a digital word (PCM word or codeword). • Each quantized sample is digitally encoded into an l bits codeword where L in the number of quantization levels and

  8. Quant. levels boundaries x(nTs): sampled values xq(nTs): quantized values Quantization example amplitude x(t) 111 3.1867 110 2.2762 101 1.3657 100 0.4552 011 -0.4552 010 -1.3657 001 -2.2762 000 -3.1867 Ts: sampling time t PCM codeword 110 110 111 110 100 010 011 100 100 011 PCM sequence

  9. Process of quantizing noise Qauntizer Model of quantizing noise AGC + Quantization error • Quantizing error: The difference between the input and output of a quantizer

  10. Uniform q. Quantization error … • Quantizing error: • Granular or linear errors happen for inputs within the dynamic range of quantizer • Saturation errors happen for inputs outside the dynamic range of quantizer • Saturation errors are larger than linear errors • Saturation errors can be avoided by proper tuning of AGC • Quantization noise variance:

  11. Uniform and non-uniform quant. • Uniform (linear) quantizing: • No assumption about amplitude statistics and correlation properties of the input. • Not using the user-related specifications • Robust to small changes in input statistic by not finely tuned to a specific set of input parameters • Simply implemented • Application of linear quantizer: • Signal processing, graphic and display applications, process control applications • Non-uniform quantizing: • Using the input statistics to tune quantizer parameters • Larger SNR than uniform quantizing with same number of levels • Non-uniform intervals in the dynamic range with same quantization noise variance • Application of non-uniform quantizer: • Commonly used for speech

  12. compression+expansion companding Non-uniform quantization • It is done by uniformly quantizing the “compressed” signal. • At the receiver, an inverse compression characteristic, called “expansion” is employed to avoid signal distortion. Compress Quantize Expand Channel Transmitter Receiver

  13. Transmitting Analog Data with Digital Signals • To convert analog data into a digital signal, there are two basic techniques: • Pulse code modulation (used by telephone systems) • Delta modulation

  14. Pulse Code Modulation • Analog waveform is sampled at specific intervals • “Snapshots” are converted to binary values

  15. Pulse Code Modulation (continued) • Binary values are later converted to an analog signal • Waveform similar to original results

  16. Pulse Code Modulation (continued) • The more snapshots taken in the same amount of time, or the more quantization levels, the better the resolution

  17. Pulse Code Modulation (continued) • Because the human voice has a fairly narrow bandwidth • Telephone systems digitize voice into either 128 levels or 256 levels • Called quantization levels • If 128 levels, then each sample is 7 bits (2 ^ 7 = 128) • If 256 levels, then each sample is 8 bits (2 ^ 8 = 256)

  18. Pulse Code Modulation (continued) • How fast do you have to sample an input source to get a fairly accurate representation? • Nyquist says 2 times the bandwidth • Thus, if you want to digitize voice (4000 Hz), you need to sample at 8000 samples per second

  19. Delta Modulation • An analog waveform is tracked using a binary 1 to represent a rise in voltage and a 0 to represent a drop

  20. Source Coding • To eliminate redundancy • Huffman Coding • Shannon-Fano Coding • To maximize information rate in a transmission • What is Information Rate ? • Information per bit  Entropy

  21. Channel Coding • Error Control Coding • To reduce the impact of channel errors by controlled introduction of redundancy • Decrease in effective data rate • Increased coding gain • Forward Error Correcting Codes • Linear Block Codes • Convolutional Codes • ARQ methods

More Related