Modulation and coding course- lecture 2

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Mportant features of digital communication systems - Some basic concepts and definitions as signal classification, spectral density, random process, linear systems and signal bandwidth.

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Nội dung Text: Modulation and coding course- lecture 2

Digital Communications I: Modulation and Coding Course Period 3 – 200/ Catharina Logothetis Lecture 2
Last time, we talked about: Important features of digital communication systems Some basic concepts and definitions as signal classification, spectral density, random process, linear systems and signal bandwidth. Lecture 2 2
Today, we are going to talk about: The first important step in any DCS: Transforming the information source to a form compatible with a digital system Lecture 2 3
Formatting and transmission of baseband signal Digital info. Textual Format source info. Pulse Analog Transmit Sample Quantize Encode modulate info. Pulse Bit stream waveforms Channel Format Analog info. Low-pass Decode Demodulate/ filter Receive Textual Detect sink info. Digital info. Lecture 2 4
Format analog signals To transform an analog waveform into a form that is compatible with a digital communication, the following steps are taken: 1. Sampling 2. Quantization and encoding 3. Baseband transmission Lecture 2 5
Sampling Time domain Frequency domain xs (t ) = xδ (t ) × x(t ) X s ( f ) = Xδ ( f ) ∗ X ( f ) x(t ) | X(f )| xδ (t ) | Xδ ( f ) | xs (t ) | Xs( f )| Lecture 2 6
Aliasing effect LP filter Nyquist rate aliasing Lecture 2 7
Sampling theorem Analog Sampling Pulse amplitude signal process modulated (PAM) signal 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. Lecture 2 8
Quantization Amplitude quantizing: Mapping samples of a continuous amplitude waveform to a finite set of amplitudes. Out In Average quantization noise power Quantized Signal peak power values Signal power to average quantization noise power Lecture 2 9
Encoding (PCM) A uniform linear quantizer is called Pulse Code Modulation (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 Lecture 2 10
Quantization example amplitude x(t) 111 3.1867 110 2.2762 Quant. levels 101 1.3657 100 0.4552 011 -0.4552 boundaries 010 -1.3657 001 -2.2762 x(nTs): sampled values xq(nTs): quantized values 000 -3.1867 Ts: sampling time PCM t codeword 110 110 111 110 100 010 011 100 100 011 PCM sequence Lecture 2 11
Quantization error Quantizing error: The difference between the input and output of a quantizer e(t ) = x(t ) − x(t ) ˆ Process of quantizing noise Qauntizer Model of quantizing noise y = q( x) AGC x(t ) ˆ x(t ) x(t ) ˆ x(t ) x e(t ) + e(t ) = x(t ) − x(t ) ˆ Lecture 2 12
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: ∞ σ = E{[ x − q( x)] } = ∫ e 2 ( x) p( x)dx = σ Lin + σ Sat 2 q 2 2 2 −∞ L / 2 −1 ql2 q2 σ 2 Lin =2∑ p ( xl )ql Uniform q. σ Lin 2 = l =0 12 12 Lecture 2 13
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 Lecture 2 14
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. compression+expansion companding y = C ( x) ˆ x x(t ) y (t ) ˆ y (t ) ˆ x(t ) x ˆ y Compress Qauntize Expand Transmitter Channel Receiver Lecture 2 15
Statistical of speech amplitudes In speech, weak signals are more frequent than strong ones. Probability density function 1.0 0.5 0.0 1.0 2.0 3.0 Normalized magnitude of speech signal ⎛S⎞ Using equal step sizes (uniform quantizer) gives low ⎜ N ⎟ for weak ⎝ ⎠q signals and high ⎛ ⎞ for strong signals. S ⎜ ⎟ ⎝ N ⎠q Adjusting the step size of the quantizer by taking into account the speech statistics improves the SNR for the input range. Lecture 2 16
Baseband transmission To transmit information through physical channels, PCM sequences (codewords) are transformed to pulses (waveforms). Each waveform carries a symbol from a set of size M. Each transmit symbol represents k = log 2 M bits of the PCM words. PCM waveforms (line codes) are used for binary symbols (M=2). M-ary pulse modulation are used for non-binary symbols (M>2). Lecture 2 17
PCM waveforms PCM waveforms category: Nonreturn-to-zero (NRZ) Phase encoded Return-to-zero (RZ) Multilevel binary +V 1 0 1 1 0 +V 1 0 1 1 0 NRZ-L -V Manchester -V Unipolar-RZ +V Miller +V 0 -V +V +V Bipolar-RZ 0 Dicode NRZ 0 -V -V 0 T 2T 3T 4T 5T 0 T 2T 3T 4T 5T Lecture 2 18
PCM waveforms … Criteria for comparing and selecting PCM waveforms: Spectral characteristics (power spectral density and bandwidth efficiency) Bit synchronization capability Error detection capability Interference and noise immunity Implementation cost and complexity Lecture 2 19
Spectra of PCM waveforms Lecture 2 20