1. Turbo Codes
Azmat Ali Pasha

2. Goals of Presentation Why Coding, Error Correction, etc?
Basic terms and concepts
Methods of handling the noise issues

3. Error Control Coding/Channel Coding
What can you do in situations where data is transmitted over a noisy channel?
Adding redundancy to information
Check code
Correct Errors

4. Transmission Data is digitally recorded and compressed
Data is encoded by error control coding
Data is modulated from digital data to an analog signal

5. Reception
Analog signal is received and demodulated back to a digital signal
Data is processed in the Error Control Decoder
Redundancy is used to check for errors and correct them
Data is uncompressed and presented

6. Transmission Process with Coding
Application Layer 
Data Compression
Application Layer 
Data Decompression
Convolutional or
Turbo coding
Viterbi or
Turbo decoding
Channel Coding
Channel Decoding
Modulation 
Frequency Up-conversion
Power Amplification
Demodulation 
Frequency Down-conversion
Receiver

7. Sensitivity to Error Media Sensitivity to Error Uncompressed Voice
Low Sensitivity
Uncompressed Video
Compressed Voice
High Sensitivity
Compressed Video
Data

8. Repetition Code Simple Repetition Code Problems with Repetition
Information Sequence {010011}
Codeword { }
Code-rate = ½
Problems with Repetition
Bandwidth Increase
Decrease the information rate

9. Channel Coding When NOT to channel code! Best case Channel Coding
Transmitter power is irrelevant
No noise in the channel
Best case Channel Coding
Shannon Limit (ideal)
Shannon hasn’t been reached yet (Turbo codes are the closest)

10. Code Performance Bit Error Rate (BER) Signal to Noise Ratio (SNR)
Probability of any particular bit being in error in a transmission
Signal to Noise Ratio (SNR)
The ratio of channel power to the noise power
Best Case
Low BER (fewer errors in final data)
Low SNR (less power req.)

11. Coding System Comparison

12. Error Correction Codes
Block
Convolutional
Turbo code
Technically a block code
Works like both Block and Convolutional codes

13. Block Code Most common is Hamming Code
Take a block of length, k (information sequence)
Then encode them into a codeword, the last (n-k) bits are called parity bits
Parity bits used for error checking and correcting

14. Block Code (2D Mapping)
higher minimum weight of code, higher the minimum weight between valid code words
higher weight, better decoder performance

15. Convolutional Codes Continuous or Streaming coding
Viterbi and Soft Output Viterbi are the most common

16. Turbo Codes Mix between Convolutional and Block codes
Require a Block code
HOWEVER, they use shift registers like Convolutional Codes

17. Turbo Codes (contd.)
Most common is the PCCC (Parallel Concatenated Convolutional Codes)
Produce high weight code words
Interleaver shuffles the input sequence, uk, in such a way that it produces a high weight

18. Turbo Code Decoder It requires a soft output decoder Soft-output
Assign a probability to decoded information (eg. 1 with a 80% likelihood)
Outperform hard decision algorithms
MAP (Maximum A Posteriori)

19. Iterative Decoding

20. Turbo Decoding Cycle will continue until certain conditions are met
The decoder circulates estimates of the sent data like a turbo engine circulates air
Once the decoder is ready, the hard decision is made

21. Error Corrections Old and New

22. Uses Cell Phone Satellite Communication Dial-up Communication
RF Communication (AutoID? WiFi?)

23. Questions, Clarifications, and Comments
Turbo Coding Method?
Business Implications?
Reduced Power Requirements
Higher Bandwidth (lower redundancy)