通信原理课件西南交大版

1、Southwest Jiaotong University 西南交通大学现代通信与信息理论Modern Communication and Information TheoryPresented By (in order of lectures)Li Hao(郝莉), PENG Daiyuan (彭代渊)，LUO Bin(罗斌), HE Dake(何大可), FANG Xuming(方旭明), WANG Hongxia(王宏霞), FENG Quanyuan(冯全源), PAN Wei(潘炜)IntroductionLi Hao (郝莉, Email: )September 2008 Janu

2、ary 2009, SWJTU-IMC 2008eCCCCONTENTSIntroduction: Course Description and OverviewGeneralized Shannons Sampling Theory Shannon Coding Theorems & Recent Advances Opportunistic Comms & Multi-user DiversityOptimization Theory & Algorithms for CommsQuantum Information & CodingSelected Topics in Statistic

3、al CommunicationsTheory of Chaos and FractalRecent Advances of Radio and Antenna TheoryTheory of Modern Data Compression Suitability and Coverage: The course is open to PhD students majoring in information and communication engineering. This an advanced course covering selected important topics in c

4、ommunications and information theory. Prerequisite: Communications principles and higher mathematics.Grading Method: Grades are based on the following weights. Course exam: 60% Homework and classroom discussions: 40% Final course grading will not be on an absolute scale. Students participation in an

5、d after class may affect the final grade. Course DescriptionHomework Policy: Several written homework assignments will be given throughout the course. These assignments will cover the reading and the class material. To be eligible for credit, each assignment solution must fulfill the published requi

6、rements and must be completed by the due date. Homework should normally be submitted electronically school website. Deadline Policy: All homework assignments will be given with a strict deadline, and students are required to submit their assignments on or before the deadline. In case of extenuating

7、circumstances, students are advised to contact the instructor as soon as practical. Conduct Policy: We pledge to hold ourselves and our peers to the highest standards of honesty and integrity. All assignments are to be considered an individual effort unless otherwise specified by the instructor. Stu

8、dents are expected to read, understand, and follow the honour code rules as set forth by the university. Course Policy Date of Birth: 1916.4.30 (-2001.2.24) Date of place: Gaylord, Michigan, USA Height: 178cm, weight: 68kg Wife: Mary Elizabeth (Betty) Moore with 3 children Education: B.S. 1936, Univ

9、. Mich.; M.S.(“Boolean algebra and switching”), 1938, MIT; PhD (“An algebra for theoretical genetics”), 1940, MIT. Interests: Comms, switching, artificial intelligence, games, etc. Hobbies: Building gadgets, juggling, unicycling. Awards: FIEEE, Academician of Sciences/Art (USA), 1966 National Medal

10、of Science, 1972 Harvey Prize, 1985 Kyoto Prize. Favorite award: 1940 American Institute of Elec. Engineers award for masters thesis.Claude Elwood Shannon Shannon is an inventor, linkerer, puzzle-solver, prankster and the father of information theory, as labeled by his colleagues. Shannons classic p

11、aper received a negative review from a prominent mathematician. Information theory has sometimes been criticized for ignoring transmission delay and decoding complexity. The interpretation of information in Shannons theory had nothing to do with the “meaning” of the message, but simply a measure of

12、the inherent requirements involved in communicating that message as one of a set of possible messages.Shannon & Information Theory Information & coding (source coding, noisy channel coding, etc): C. E. Shannon, A mathematical theory of communication theory, Part 1&2, Bell Syst. Tech. J., Vol.27, pp.

13、379-423,623-656, 1948. Sampling & AWGN channel results (geometrically): C.E. Shannon, Communication in the presence of noise, Proceedings of IRE, Vol.37, pp.10-21, Jan. 1949. Cryptography: C. E. Shannon: “Communication theory of secrecy systems,” Bell Syst. Tech. J., Volume 28, 656-715, 1949. Source

14、 coding & rate distortion: C. E. Shannon, Coding theorems for a discrete source with a fidelity criterion, IRE Nat. Conv. Rec., pp.142-163, 1959. Multi-user information theory: C. E. Shannon, Two-way communication channels, in Proc. 4th Berkeley Symp. Prob. Stat. Berkeley, CA: Univ. California Press

15、, 1961, vol.1, pp. 611644.Information Theory & CommunicationsC.E. Shannon & F. Jin at ISIT1985Overview of the CourseIntroduction (Li Hao, 1Hr)Generalized Shannons Sampling Theory (Li Hao, 2+3Hr)Shannon Coding Theorems & Recent Advances (PENG Daiyuan, 23Hr)Multi-user Information Theory (Li Hao, 13Hr)

16、Optimization Theory & Algorithms for Communications (LUO Bin, 23Hr)Theory of Modern Data Compression (HE Dake, 23Hr)Selected Topics in Statistical Communications (FANG Xuming, 23Hr)Theory of Chaos and Fractal (Wang Hongxia, 23Hr)Recent Advances of Radio and Antenna Theory (FENG Quanyuan, 23Hr)Quantu

17、m Information & Coding (PAN Wei, 23Hr)Generalized Shannons Sampling TheoryClassical Shannons sampling theory: sampling and dirac distributions; sampling with and without prefiltering; standard three-step sampling paradigm.Limitations of Shannons formulation of sampling theorem: bandlimited; ideal lo

18、w-pass filter; slow decay of sinc(x) function.Error analysis in sampling representation: truncation error; aliasing error, jitter error, round-off error, amplitude error. Generalized Shannons model and sampling: from sinc(x) to general generating function (x); minimum error sampling; sampling with n

19、onideal acquisition, equivalent basis function.Multidimensional and other extensions of sampling theory: multidimensional; multi-wavelets and finite elements; derivative and interlaced sampling; frames; irregular sampling.Shannon Coding Theorems & Recent Advances Shannons source coding theorem: sour

20、ce coding theorem for the discrete memoryless source (DMS); source coding theorem for the Gaussian source. Shannons channel coding theorem: channel coding theorem for DMCs; coding theorem for Gaussian channels; weak converse to the Noisy coding theorem ; strong converse to the Noisy coding theorem.

21、Source-channel coding theorem: rate-distortion function of the source; capacity-cost function of the channel; source-channel coding theorem. Communication theory of secrecy systems: schematic of a general secrecy system; perfect secrecy; equivocation; ideal secrecy systems; practical secrecy. Recent

22、 Advances.Opportunistic Comms & Multi-user Diversity Optimization Theory & Algorithms for Communications Introduction: optimization problems; optimization algorithms; computing complexity and pleteness. Classical Algorithms: recursion; divide and conquer; dynamic programming; greedy algorithms; back

23、tracking method; branch and bound methods. Intelligent Optimization Algorithms: simulated annealing; genetic algorithms; tabu search algorithms; neural networks optimization algorithms; neighborhood region search algorithms. Applications of Optimization Algorithms: numerical calculation; communicati

24、ons simulation.Quantum Information & CodingIntroduction and overview: global perspectives; history of quantum computation and information; quantum information theory; future directions. Quantum computation: quantum gates and circuits; classical computation and quantum algorithms; Bell states; quantu

25、m teleportation.Quantum information: quantum information theory; Von Neumann entropy and Holevo bound; Schumachers quantum noiseless channel coding theorem; Holevo-Schmacher-Westmoreland theorem; information theoretic relations; quantum cryptography; teleportation and dense coding. Physical realizat

26、ions: ions in traps; solid-state devices; nuclear magnetic resonance.Decoherence, errors & error correction: decoherence and other error sources; detection and correction of errors; fault-tolerant computing.Selected Topics in Statistical CommunicationsIntroduction: Basic concepts on teletraffic theo

27、ryDiscrete Markov Chain & Birth-Death Process Queuing Systems: Birth-Death Processes, Birth-Death Statistical Equilibrium etc.Teletraffic theory with Multiple Traffic Types: Different traffic characteristics, Resource allocation schemes, Simple and multidimensional markov systems modeling and perfor

28、mance analysisTeletraffic Modeling for Wireless Networks: New initiated call and handoff call differentiation and considerationQoS Provisioning in Broadband IP Networks: Wireless QoS, GoS and Cost.Regression Models and Long-Range Dependent Traffic Models: New results on multimedia traffic modeling a

29、nd analysis.SummaryTheory of Chaos and Fractal Chaotic theory: concepts; nonlinear dynamics; Lyapunov exponents; bifurcation; chaotic attractors; characteristic; models of chaotic systems: 1-D,2-D,3-D,hyper-chaos. Fractal: concepts; iterate function system (IFS); generation mechanism; fractal dimens

30、ions; self-affine of fractal; relation of chaos and fractal; application. Chaotic control: OGY method; perturb parameters; pilot orbit. Chaos & Secure communications: comparison between classical and chaotic encryption; chaotic behavior of neural network; synchronization; application in frequency ho

31、pping / spread-spectrum (FH/SS) communications. Chaos & Information hiding: introduction; data hiding; chaotic digital watermark: robust / fragile / semi-fragile watermarking; application fields; recent advances.Recent Advances of Radio and Antenna TheoryIntroduction: Basic concepts on electromagnetic wave and antenna theoryPropagation: radio research problems, propagation in mobile communicationsmobile antenna systems : the basic technology of mobile antenna system design, DBS(Direc