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Coding technology

This course explores the fundamentals of ICT processing, including big data networking, storage in cloud computing, reliability in channel coding, efficiency in source coding, and security in cryptography. Learn how to turn raw data into structured data in the most reliable and efficient way. Gain insights into modern coding technologies and algorithms that can overcome the limits of available resources. Increase your understanding of spectral efficiency and the achievable data transmission rate. Join us in this comprehensive coding technology course to unlock the potential of data and intelligence.

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Coding technology

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  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 (QBF10) • Friday 10.15-12.00 (odd weeks, QBF10) 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. Modern Information technologies=A PATH FROM DATA TO INTELLIGENCE Scope – fundamentals of ICT Porcessing: Big Data Networking (IoT, WSN ..etc.) Storage: cloud computing How to turn raw data into structured data in the most reliable and efficient way ? Reliability (Channel Coding) Efficiency (Source Coding) Security (Cryptography)

  5. The basic problem of CE: resources vsQoS Present day communication technologies Wired (IPV4, IPV6 on WDM fiber optics platform ) Wireless (4G/5G, Blue Tooth, WiFi …etc.) “Infinite” data speed, ARQ (ACK/NAK protocol), energy and data speed are not a problem Limited data speed (narowband radio spectrum), low quality channel (multiptah propagation, 5G), energy matters a lot Channel Coding • Resources: • bandwidth, • transmission power • Quality of Service (QoS): • dataspeed, • Bit Error Rate (BER) min constraint

  6. Modern coding 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 !!!

  7. 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 Theoretical limit of SE : channel dependent appr. 5 Bit/sec/Hz Coding thechnology: by what algorithms can one achieve these theoretical limits ?

  8. THANK YOU FOR YOR ATTENTION !

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