Formatting and Baseband Modulation. Dr. Syed Anwar Lecture 3&4 Digital Communication

Transcription

1 Formatting and Baseband Modulation Dr. Syed Anwar Lecture 3&4 Digital Communication

2 Formatting A transformation on the message or source signal to make it compatible with digital processing. Source Coding Data compression in addition to formatting Digital messages (0/1) transformed using pulse modulation into pulses (baseband waveforms) that can be transmitted over cables.

3 Baseband Systems

4 Textual Information Alphanumeric data encoded using one of the widely used character encoding formats (ASCII, EBCDIC) Character Coding Transform text into bits. ASCII 7-bit code to encode 128 characters (8-bit with parity check)

5 Textual Information Textual Message --- Alphanumeric characters Characters --- encoded into a bit stream (baseband signal) Symbols --- formed by grouping k bits, resulting in a symbol alphabet M with 2 k symbols. M-ary system --- system using a symbol set of size M, for k = 1, system is termed as binary with symbol size M = 2.

6 Textual Encoding (Example) You want to transmit the word UET using an 8- ary system, Encode using 7-bit ASCII, followed by an eight bit for error detection. How many bits are there in the message? For k = 3, how many octal symbols are there in the message? If the system were designed with 16-ary modulation, how many symbols would be needed to represent UET?

7 Analog Information Sampling --- produce a discrete pulse amplitude modulated (PAM) waveform. Sampling Theorem A band-limited signal having no spectral components above f m HZ, can be determined uniquely by values sampled at uniform intervals of, uniform sampling theorem.

8 Nyquist Criterion The sampling theorem can be stated in terms of Nyquist criterion as The sampling rate f s =2 f m is called the Nyquist rate, theoretically a sufficient condition for a complete analog signal reconstruction. Different sampling approaches, Impulse sampling Natural sampling Sample-and-Hold operation

9 Impulse sampling Ideal sampling with a sequence of unit impulse functions. The periodic train of impulse functions can be defined as, The analog signal is multiplied by the impulse train, using the sifting property of impulse function. The multiplication in time domain is converted to frequency domain using frequency convolution property.

10 Natural Sampling Multiply the analog signal with a pulse train having pulses with width T and amplitude 1/T. Equivalent to opening and closing of a switch. The sampled signal can be expressed as, Natural sampling Top of each pulse in x s (t) retains the shape of its corresponding analog segment during the pulse interval. Uses the frequency shifting property of Fourier transform

11 Sample and Hold Convolution of sampled impulse train with a unity amplitude rectangular pulse of width Ts The hold operation results in attenuation of higher-frequency spectral components. Non uniform spectral gain

12 Aliasing Results from undersampling Aliased spectral components represent ambiguous data in the frequency band. Can be eliminated by employing higher sampling rate (minimum criteria = nyquist rate)

13 Aliasing Aliasing eliminated using anti-aliasing filters Pre-filtering the new maximum frequency of the analog signal is reduced to f m, such that it is less than or equal to f s /2. Post-filtering Use a low pass filter on the sampled data Both filtering techniques results in signal information loss

14 Transition Bandwidth Finite bandwidth of realisable filters for transition between passband and out-of-band attenuation. Narrower transition bandwidth Filter complexity and cost Higher sampling rate Storage and transmission rates For 20% transition bandwidth for anti-aliasing filters, we have

15 Aliasing

16 Oversampling The process of analog to digital conversion with and without oversampling can be broken down into following steps Without oversampling Limit the signal bandwidth by using a high performance analog lowpass filter, Sample at nyquist rate for (approximately) bandlimited signal, Process the samples using analog-to-digital converter.

17 Oversampling With Oversampling Use a low performance analog low pass filter to limit the bandwidth, The pre-filtered (approximated) bandlimited signal is sampled at (a higher) Nyquist rate, Samples are processed using analog-to-digital converter Digital samples processed using high performance digital filter to reduce the bandwidth Sample rate at the output of digital filter reduced in proportion to the bandwidth reduction.

18 Analog-to-Digital a) Analog b) PAM c) Quaitisation d)sample and Hold

19 Sources of Corruption Sampling and Quantisation Quantisation noise Quantisation Saturation Timing jitter Channel Effects Channel noise ISI

20 Quantisation Noise Approximate the analog signal with quantised samples Round off or truncation error Inversely proportional to the number of levels employed in the quantisation process.

21 Sampling and Quantisation Quantiser Saturation The range of inputs for which the difference between input and output is small is called the operating range of the converter If the input exceeds this range the converter is said to be operating in saturation. Timing Jitter According to sampling theory, exact reconstruction possible when the signal sampled uniformly. Timing jitter results in non uniform sampling, a random process

22 Channel Effects Channel Noise Thermal, interference from other users, interference from circuit switching transients Threshold effect: Rapid degradation of output signal with channel induced errors. Intersymbol Interference Band limited channels Spreads the waveforms passing through such channels Causes signals to overlap and hence system degradation

23 SNR for Quantised Pulses

24 Assignment 1 Hand In Before Wednesday , 8:40 AM No late Hand In, Plagiarism Assignment should be handwritten