1. Coding technology Lecturer:
Prof. Dr. János LEVENDOVSZKY
Course website:

2. Course information REQUIREMENTS:
LECTURES:
Thursday
Friday
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 Information Theory, John Wiley, (IT)
S. Verdu, S. Mclaughlin: Information Theory: 50 years of discovery, IEEE, (IT)
D. Costello: Error control codes, Wiley, 2005
S. Golomb: Basic Concepts in Information Theory and Coding, Kluwer, (IT + CT)
E. Berlekamp: Algebraic Coding Theory. McGraw Hill, (CT)
R.E. Blahut: Theory and Practice of Error Correcting Codes. Addison Wesley, (CT)
J.G. Proakis: Digital communications,McGraw Hill, 1996

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

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

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

7. Why to enhance the performance of wireless communication systems ?
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 !
E.g. - low BER requires increased transmission power
- higher data rate requires more radio spectrum
Solution: develop intelligent algorithms to overcome these limitations !!! 7

8. Replacing resources by algorithms !!!
General objective
Replacing resources by algorithms !!!
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

9. Frequency allocation
TÁMOP – /2/A/KMR 9
TÁMOP – /2/A/KMR 9

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

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. Theoretical endeavours inspired by technology and algorithmic solutions
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)
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. Limited resources (transmission power, bandwidth …etc.)
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. # of bits appr. One-fourth
Source coding
0000
0001
0010
0011
0100
0101
1111
symbols
codewords a1 01 a2
10111 a3
111 a4
110 aN
01110
Optimal codetable ?
…………0000
…………0
# of bits appr. One-fourth

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

16. Cryptography Public channel
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. Primer info (voice, image..etc.)
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?
Corresponding algorithms:
Coding technology

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
BSC’
errors 1
00000
01010 5x
BSC
Majority dec.
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 !