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DIGITAL COMMUNICATIONS
LECTURE NOTES
B.TECH
(III YEAR – II SEM)
Prepared by:
Dr.M.Sucharitha, Professor
Mrs. P. Swetha, Assistant Professor
Mrs. S. Rajani, Assistant Professor
Department of Electronics and Communication Engineering
MALLA REDDY COLLEGE
OF ENGINEERING & TECHNOLOGY
(Autonomous Institution – UGC, Govt. of India)
Recognized under 2(f) and 12 (B) of UGC ACT 1956 (Affiliated to JNTUH, Hyderabad, Approved by AICTE - Accredited by NBA & NAAC – ‘A’ Grade - ISO 9001:2015 Certified) Maisammaguda, Dhulapally (Post Via. Kompally), Secunderabad – 500100, Telangana State, India
MALLA REDDY COLLEGE OF ENGINEERING AND TECHNOLOGY
III Year B.Tech. ECE-II Sem L T/P/D C 4 1/ - /- 3 (R15A0413) DIGITAL COMMUNICATIONS OBJECTIVES :
- To understand the building blocks of digital communication system.
- To prepare mathematical background for communication signal analysis.
- To understand and analyze the signal flow in a digital communication system.
- To analyze error performance of a digital communication system in presence of noise and other interferences. UNIT I Pulse Digital Modulation: Elements of digital communication systems, advantages of digital communication systems, Elements of PCM: Sampling, Quantization & Coding, Quantization error, Companding in PCM systems. Differential PCM systems (DPCM). Time Division Multiplexing & Demultiplexing. Delta Modulation: Delta modulation, its draw backs, adaptive delta modulation, comparison of PCM and DM systems, Noise in PCM and DM systems. Illustrative Problems. UNIT II Digital Modulation Techniques: Introduction, ASK modulator, Coherent and Non-Coherent ASK detector, FSK modulator, Spectrum of FSK, coherent reception, non-coherent detection of FSK. BPSK transmitter, Coherent reception of BPSK, DPSK, QPSK. Data Transmission: Base band signal receiver, probability of error, The optimum filter, Matched filter, probability of error using matched filter.Optimum filter using correlator.Probability of error of ASK,FSK,BPSK and QPSK. Illustrative Problems. UNIT III Information Theory: Discrete messages, Concept of amount of information and its properties. Average information, Entropy and its properties. Information rate, Mutual information and its properties, Illustrative Problems. Source Coding: Introduction, Advantages, Hartley Shannon’s theorem, bandwidth – S/N trade off, Shanon- Fano coding, Huffman coding, Illustrative Problems. UNIT IV Linear Block Codes: Introduction, Matrix description of Linear Block codes, Error detection and error correction capabilities of linear block codes, Hamming codes. Cyclic Codes: Encoding, Syndrome Calculation, Decoding, UNIT V Convolution Codes: Introduction, encoding of convolution codes, time domain approach, transform domain approach. Graphical approach: State, Tree and Trellis diagram. Decoding using Viterbi algorithm Illustrative Problems. TEXT BOOKS:
- Digital communications - Simon Haykin, John Wiley, 2005
- Principles of Communication Systems – H. Taub and D. Schilling, TMH, 2003 REFERENCES:
- Digital and Analog Communication Systems – K.Sam Shanmugam, John Wiley, 2005.
UNIT- 1 Digital Pulse Modulation
Elements of Digital Communication Systems:
Fig. 1 Elements of Digital Communication Systems
1. Information Source and Input Transducer: The source of information can be analog or digital, e.g. analog: audio or video signal, digital: like teletype signal. In digital communication the signal produced by this source is converted into digital signal which consists of 1′s and 0′s. For this we need a source encoder. 2. Source Encoder: In digital communication we convert the signal from source into digital signal as mentioned above. The point to remember is we should like to use as few binary digits as possible to represent the signal. In such a way this efficient representation of the source output results in little or no redundancy. This sequence of binary digits is called information sequence. Source Encoding or Data Compression: the process of efficiently converting the output of whether analog or digital source into a sequence of binary digits is known as source encoding.
3. Channel Encoder: The information sequence is passed through the channel encoder. The purpose of the channel encoder is to introduce, in controlled manner, some redundancy in the binary information sequence that can be used at the receiver to overcome the effects of noise and interference encountered in the transmission on the signal through the channel. For example take k bits of the information sequence and map that k bits to unique n bit sequence called code word. The amount of redundancy introduced is measured by the ratio n/k and the reciprocal of this ratio (k/n) is known as rate of code or code rate. 4. Digital Modulator: The binary sequence is passed to digital modulator which in turns convert the sequence into electric signals so that we can transmit them on channel (we will see channel later). The digital modulator maps the binary sequences into signal wave forms , for example if we represent 1 by sin x and 0 by cos x then we will transmit sin x for 1 and cos x for 0. ( a case similar to BPSK) 5. Channel: The communication channel is the physical medium that is used for transmitting signals from transmitter to receiver. In wireless system, this channel consists of atmosphere , for traditional telephony, this channel is wired , there are optical channels, under water acoustic channels etc.We further discriminate this channels on the basis of their property and characteristics, like AWGN channel etc. 6. Digital Demodulator: The digital demodulator processes the channel corrupted transmitted waveform and reduces the waveform to the sequence of numbers that represents estimates of the transmitted data symbols. 7. Channel Decoder: This sequence of numbers then passed through the channel decoder which attempts to reconstruct the original information sequence from the knowledge of the code used by the channel encoder and the redundancy contained in the received data Note: The average probability of a bit error at the output of the decoder is a measure of the performance of the demodulator – decoder combination. 8. Source Decoder: At the end, if an analog signal is desired then source decoder tries to decode the sequence from the knowledge of the encoding algorithm. And which results in the approximate replica of the input at the transmitter end.
Introduction to Pulse Modulation
What is the need for Pulse Modulation? Many Signals in Modern Communication Systems are digital Also, analog signals are transmitted digitally. Reduced distortion and improvement in signal to noise ratios. PAM, PWM, PPM, PCM and DM. In CW modulation schemes some parameter of modulated wave varies continuously with message. In Analog pulse modulation some parameter of each pulse is modulated by a particular sample value of the message. Pulse modulation is of two types o Analog Pulse Modulation Pulse Amplitude Modulation (PAM) Pulse width Modulation (PWM) Pulse Position Modulation (PPM) o Digital Pulse Modulation Pulse code Modulation (PCM) Delta Modulation (DM)
Pulse Code Modulation:
Three steps involved in conversion of analog signal to digital signal Sampling Quantization Binary encoding
Fig. 2 Conversion of Analog Signal to Digital Signal Note: Before sampling the signal is filtered to limit bandwidth.
Elements of PCM System :
Fig. 3 Elements of PCM System Sampling: Process of converting analog signal into discrete signal. Sampling is common in all pulse modulation techniques
Natural sampling: The spectrum is weighted by a sinc function. Amplitude of high frequency components reduces. Flat top sampling: Here top of the samples remains constant. In the spectrum high frequency components are attenuated due sinc pulse roll off. This is known as Aperture effect. If pulse width increases aperture effect is more i.e. more attenuation of high frequency components. Sampling Theorem:
PCM Generator:
Transmission BW in PCM:
Quantization is representing the sampled values of the amplitude by a finite set of levels, which means converting a continuous-amplitude sample into a discrete-time signal Both sampling and quantization result in the loss of information. The quality of a Quantizer output depends upon the number of quantization levels used. The discrete amplitudes of the quantized output are called as representation levels or reconstruction levels. The spacing between the two adjacent representation levels is called a quantum or step-size. There are two types of Quantization o Uniform Quantization o Non-uniform Quantization. The type of quantization in which the quantization levels are uniformly spaced is termed as a Uniform Quantization. The type of quantization in which the quantization levels are unequal and mostly the relation between them is logarithmic, is termed as a Non-uniform Quantization. Uniform Quantization:
- There are two types of uniform quantization.
- Mid-Rise type
- Mid-Tread type.
- The following figures represent the two types of uniform quantization.
- The Mid-Rise type is so called because the origin lies in the middle of a raising part of the stair-case like graph. The quantization levels in this type are even in number.
- The Mid-tread type is so called because the origin lies in the middle of a tread of the stair-case like graph. The quantization levels in this type are odd in number.
- Both the mid-rise and mid-tread type of uniform quantizer is symmetric about the origin.
Quantization Noise and Signal to Noise ratio in PCM System:
