1 / 23

Coding technology

Coding technology. Lecturer: Prof . Dr. János LEVENDOVSZKY (levendov@hit.bme.hu) Course website: www.hit.bme.hu/~ceffer/kodtech. Course information. LECTURES: Thursday 14.15-16.00 (R516) Friday 10.15- 12.00 (QBF11). REQUIREMENTS: One major tests (with recap possibility )

donaldpatton
Download Presentation

Coding technology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Coding technology Lecturer: • Prof. Dr. János LEVENDOVSZKY (levendov@hit.bme.hu) • Course website: www.hit.bme.hu/~ceffer/kodtech

  2. Course information • LECTURES: • Thursday 14.15-16.00 (R516) • Friday 10.15-12.00 (QBF11) REQUIREMENTS: • One major tests (with recap possibility) • Signature is secured if and only if the grade of the test (or its recap) are higher (or equal) than 2 ! • The test is partly problem solving ! • Exam (same type of problems as in midterm test) GRADING POLICY:

  3. Suggested literature and references • T.M. Cover, A.J. Thomas: Elements of InformationTheory, John Wiley, 1991. (IT) • S. Verdu, S. Mclaughlin: Information Theory: 50 years of discovery, IEEE, 1999 (IT) • D. Costello: Errorcontrolcodes, Wiley, 2005 • S. Golomb: Basic Concepts in Information Theory and Coding, Kluwer, 1994. (IT + CT) • E. Berlekamp: AlgebraicCodingTheory. McGraw Hill, 1968. (CT) • R.E. Blahut: Theory and Practice of ErrorCorrectingCodes. AddisonWesley, 1987. (CT) • J.G. Proakis: Digital communications,McGraw Hill, 1996

  4. Monitoring and surveillance Coding technologies = e-world (systems and services) Autonomous vehicles Body sensors “Network” and “data” ! Aim of coding technologies: expanding the boundaries of networks + mining “value” out of ” data (Cloud, IoT, WSN, Big Data) Integrated financial services , algo-trading Energy cons. On-line social media Google letöltédownloads

  5. Main components of ICT Porcessing: Big Data Networking (IoT, WSN ..etc.) Storage: cloud computing Coding technologies: data communication and data compression algorithms

  6. Course objective:algorithmic skills and knowledge(coding procedures) for increasing the performance of communication systems! 2020.01.04. 6

  7. Constraints & limitations: • Limited power • Limited frequency bands • Limited Interference • Requirements: • high data speed • QoS communication (low BER and low delay) • Mobility ??? Resources (bandwidth, power …etc.) are not available ! Why to enhance the performance of wireless communication systems ? E.g. - low BER requires increased transmission power - higher data rate requires more radio spectrum Solution: develop intelligent algorithms to overcome these limitations !!! 2020.01.04. 7

  8. Modern communication technologies = smart algorithms and protocols to overcome the limits of the resources Scarce and expensive Cheap and the evolution of underlying computational technology is fast 1800/1350, 1600/1200, and 1336/1000 MIPS/MFLOPS Multibillion dollar investment $ 100 investment General objective Replacing resources by algorithms !!!

  9. Frequency allocation http://en.wikipedia.org/wiki/File:United_States_Frequency_Allocations_Chart_2003_-_The_Radio_Spectrum.jpg 2020.01.04. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 9

  10. RESOURCES: e.g. bandwidth, transmission power The question telecom companies invest money into DEMANDS (QoS): given Bit Error Rate, Data Speed QoS = f (resources) ???

  11. Spectral efficiency – a fundamental measure of performance SE [bit/sec/Hz] = what is the data transmission rate achievable over 1 Hz physical sepctrum Present mobile technologies SE ~ 0.52 bit/sec/Hz Information theory: what are the theoretical limits of SE ? (channel dependent 5 Bit/sec/Hz) Coding theory: by what algorithms can one achieve these theoretical limits ?

  12. Data compression standards: APC for voice, JPEG, MPEG • Error correcting coding: • MAC protocols (RS codes, BCH codes, convolutional codes) • Data security: Public key standards (e.g. RSA algorithm) Theoretical endeavours inspired by technology and algorithmic solutions • Source coding: how far the binary representation of information provided by data sources can be compressed • Channel coding: how to achieve reliable communication over unreliable channels • Data security: how to implement secure communication over public (multi-user) channels

  13. Basic principles noise distortion e-dropping CHANNEL Limited resources (transmission power, bandwidth …etc.) Challenge: How can we communicate reliably over an unreliable channel by using limited resoures ? CODING TECHNOLOGY CHANNEL Coding Decoding ALGORITHMS ??

  14. 1111 0101 0100 0011 0010 0001 0000 Source coding Optimal codetable ? 0000 0001 0010 0011 0100 0101 …………0000 0000 1 1 1 1 1 …………0 # of bits appr. One-fourth

  15. 00000 0 01010 Majority detector 0 5x repeat Unreliable channel What is the optimal code guaranteeing a predefined relaibility with minimum loss of dataspeed? Channel coding Unreliable channel 010010110 0110111010

  16. Cryptography attacker key key message Public channel Decypher message Cypher How can one construct small algorithmic complexity cryptography algorithms which present high algorithmic complexity for the attacker, in order to yield a given level of data security ?

  17. Corresponding algorithms: Coding technology Summary Corrupt recepetion Retrieved info Alg. Primer info (voice, image..etc.) Alg. Channel • Challenges: • What is the ultimately compressed representation of information ? • What is the data rate and by what algorithms over which can communicate reliably over unreliable channels ? • How can we communicate securely over public systems?

  18. BSC as an additive channel model Binary Symmetric Channel Error bit

  19. Extension to vectors error vector

  20. Block error probability

  21. How to achieve reliable communication over an unreliable channel errors 1 0 00000 01010 Majority dec. 5x BSC BSC’ BSC’ Problem: for the sake of reliable communication we have to decrease the data speed

  22. Reliable communication by repeaters Better QoS Loss in data speed

  23. THANK YOU FOR YOR ATTENTION !

More Related