EGR 544 Communication Theory

Transcription

1 EGR 544 Communication Theory EGR Introduction Z. Aliyazicioglu Electrical and Computer Engineering Department Cal Poly Pomona Introduction Office : Building Office Hours : M Class Folder: EGR54401 Course Webpage: Grading: Exam 1 Exam 2 Final Homework/Quiz Project Textbook: Digital Communication, 4 th Ed. John Proakis, MCGraw Hill 2000 Textbook website Cal Poly Pomona Electrical & Computer Engineering Dept. EGR

2 Introduction Prerequisites Probability and Random Processes Communication Systems Reference: [1] Introduction to Digital Communication, by Rodger E. Zeimer and Roger L. Peterson, Second Edition, Prentice Hall, [2] Digital Communications, by Bernard Sklar, Second Edition, Prentice Hall, 2001 [3] Communication Systems, Simon Haykin, 4th Ed. Wiley, 2001, ISBN [4] Probability, Random Variables, and Random Processes, A. A. Papoulis, 4th Ed., McGraw-Hill, Cal Poly Pomona Electrical & Computer Engineering Dept. EGR Definition Device transfer information from one location (time) to another location (time) Digital: Smoke, Morse Code Telegraph Analog: Commercial Radio, TV Digital: Data, Computer, HDTV Cal Poly Pomona Electrical & Computer Engineering Dept. EGR

3 Types of Communication Systems Point to Point: Telephone, Fax Point to Multipoint: Broadcast (Radio, TV) Simplex: One Way Duplex: Two Ways Cal Poly Pomona Electrical & Computer Engineering Dept. EGR Design Consideration Cost/Performance Trade Off Cost Power Bandwidth Complexity Performance Data Rate Bit Error Probability Transmission Range Fault Tolerance Adaptive to Environment Security Anti Jamming Capability Low Probability of Interception Cal Poly Pomona Electrical & Computer Engineering Dept. EGR

4 Analog Modulation Cal Poly Pomona Electrical & Computer Engineering Dept. EGR Digital Modulation Cal Poly Pomona Electrical & Computer Engineering Dept. EGR

5 Pulse Modulation Cal Poly Pomona Electrical & Computer Engineering Dept. EGR Why Study digital communications? Digital receiver needs only distinguish between two waveforms it is possible to exactly recover digital information Transmitted bits can be detected and regenerated, so Noise does not propagate additively. More signal processing techniques are available to improve system performance: source coding, channel (error-correction) coding, equalization, encryption, filtering, Better control and more flexibility... Digital ICs are inexpensive to manufacture. A single chip can be mass produced at low cost, no mater how complex Digital communications permits integration of voice, video, and data on a single system (ISDN) Implementation by software instead of hardware Security is easier to implement. Cal Poly Pomona Electrical & Computer Engineering Dept. EGR

6 Digital Communication System The basic elements of a digital communication system Information Source Source Encoder Encoder Digital Modulator Discrete Noise Output Source Decoder Decoder Digital Demodulator Information & Coding Theory Modulation & Detection Theory Cal Poly Pomona Electrical & Computer Engineering Dept. EGR Digital Communication System Source are converted into a sequence of binary digits which is called information sequence Represent the source by an efficient number of binary digits Efficiently converting the source into a sequence of binary digits is a process, which is called source encoding of data compression encoder adds some redundancy into binary information sequence that can be used for handle noise and interference effects at the receiver. Digital modulator maps the binary information sequence into signal waveforms. Communication channel is used to send the signal from the transmitter to the receiver. Physical channels: the atmosphere, wireless, optical, compact disk,. Cal Poly Pomona Electrical & Computer Engineering Dept. EGR

7 Digital Communication System Digital demodulator receives transmitted signal contains the information which is corrupted by noise Cannel decoder attempts the reconstruct the original information sequence from knowledge of the code used by channel encoder. Source decoder attempts the reconstruct the original signal from the binary information sequence using the knowledge of the source encoding methods. The difference between the original signal and the reconstructed signal is measured of the distortion introduced by the digital communication system Estimate what was send, aiming at the minimum possible probability of making mistakes Cal Poly Pomona Electrical & Computer Engineering Dept. EGR Communication channels and their characteristics Physical channel media magnetic-electrical signaled wire channel modulated light beam optical (fiber) channel antenna radiated wireless channel acoustical signaled water channel Virtual channel magnetic storage media Noise characteristic thermal noise (additive noise) signal attenuation phase distortion multi-path distortion Limitation of channel usage transmitter power receiver sensitivity channel capacity (such as bandwidth) Cal Poly Pomona Electrical & Computer Engineering Dept. EGR

8 Communication channels and their characteristics Frequency range for guided wire channel Cal Poly Pomona Electrical & Computer Engineering Dept. EGR Communication channels and their characteristics Frequency range for wireless electromagnetic channels. [Adapted from Carlson (1975), 2nd edition Cal Poly Pomona Electrical & Computer Engineering Dept. EGR

9 Communication channels and their characteristics Additive noise channel st () + nt () rt () = αst () + nt () rt () = αst () + nt () where α is the attenuation factor, s(t) is the transmitted signal, and n(t) is the additive random noise process. Called Additive Gaussian noise channel if n(t) is a Gaussian noise process. Cal Poly Pomona Electrical & Computer Engineering Dept. EGR Communication channels and their characteristics The linear filter channel with additive noise to ensure the specified bandwidth limitations. st () Linear filter c(t) + nt () rt () = st () ct () + nt () rt () = st () ct () + nt () = c( τ) s( t τ) dτ + n( t) Cal Poly Pomona Electrical & Computer Engineering Dept. EGR

10 Communication channels and their characteristics The linear time-variant filter channel with additive noise Time-variant multipath propagation. st () Linear time-variant Filter c(τ ;t) + nt () rt () = st () c(;) τ t + nt () rt () = st () c(;) τ t + nt () = c(;)( τ t s t τ) dτ + n() t where c(τ ;t) is the response of the channel time t due to an impulse applied at time t- τ. Cal Poly Pomona Electrical & Computer Engineering Dept. EGR