Principle of Communications, Fall 2017 Lecture 01 Introduction to Communication Systems I-Hsiang Wang ihwang@ntu.edu.tw National Taiwan University 2017/9/13
Outline • The basic architecture of communication systems • Source-channel separation • Overview of the course 2
A communication system Noise Source Destination Encoder Channel Decoder • The simplest point-to-point abstraction • Given by nature/applications: source, channel, destination • Engineers’ design: encoder, decoder 3
Source and channel Noise Source Destination Encoder Channel Decoder • Source ‣ generates the message to be delivered ‣ message: text, audio, image, video, etc.. → can be abstracted into signals ‣ in general, signals can be continuous-time and continuous-valued → waveforms ! • Channel ‣ abstraction of the physical medium: light, sound, wire, optical fiber, EM radiation, etc. ‣ distorts the transmitted signal in some way → noisy channel, using stochastic models 4
Encoder and decoder Noise Source Destination Encoder Channel Decoder • Encoder ‣ converts the message into physical signals that is ready for transmission ‣ functions include sampling, quantization, compression, error-correction coding, modulation, etc. • Decoder ‣ reconstructs the message from the received signals ‣ depending on the goal of the destination, can also carry out other tasks such as computing a function of the messages. 5
Digital communication system message signal waveform waveform Bits Source Channel Source Encoder Encoder Binary Noisy Interface Channel Source Channel Destination Decoder Decoder Bits • A digital communication system uses digital sequence ( bits ) as an interface between source coding and channel coding • Why separation? ‣ Digital hardware is cheap, reliable, and scalable. ‣ Source coding and channel coding can be independently designed. ‣ Source-channel separation attains optimal transmission e ffi ciency (Shannon). ‣ Bits as universal currency of the digital world → a channel can be extended to a network 6
Source coding: main building blocks Source Coding Binary Interface input Discrete { s [ m ] } { d m } (message) s ( t ) { b i } Sampler Quantizer Encoder waveform analog discrete sequence sequence output Table Discrete { ˆ s [ m ] } { ˆ d m } (message) Filter s ( t ) ˆ { ˆ b i } Lookup Decoder waveform • Sampling: (continuous-time) waveform → (discrete-time) sequence ‣ (Review: Sampling Theorem, Nyquist Rate) • Quantization: continuous-valued sequence → discrete-valued sequence • Compression: discrete-valued sequence → bit sequence, remove redundancy 7
Channel coding: main building blocks Channel Coding Binary Interface x ( t ) { c i } { u m } x b ( t ) ECC Symbol Pulse Up { b i } Encoder Mapper Shaper Converter Information coded discrete baseband passband Noisy bits bits sequence waveform waveform Channel y ( t ) { ˆ c i } { ˆ u m } y b ( t ) ECC Symbol Sampler Down { ˆ b i } Decoder Demapper + Filter Converter 8
Overview of the course: all on channel coding • Building a bridge between the cyber world and the physical world ‣ Digital modulation and demodulation without noise • Overcoming noise in narrow-band channels ‣ Optimal detection under noise ‣ Reliable communication with error correction codes • Overcoming inter-symbol interference in wide-band channels ‣ Equalization techniques ‣ Orthogonal frequency division multiplex (OFDM) • Overcoming fading in wireless channels ‣ Diversity techniques 9
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