Information and Coding Theory for Mobile Communications

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Information and Coding Theory for Mobile
Communications

• Joachim Hagenauer
• Lehrstuhl für Nachrichtentechnik (LNT)
• Technische Universität München (TUM)
• Diderot Mathematical Forum 2001
• Eindhoven, Helsinki, Lausanne

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Claude Elwood Shannon 1916 24.2.2001
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Mobile Transmission Scheme with Source and
Channel Coding
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Simplified Model of the Mobile Flat Fading Channel
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Coding Gain
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Multiple Input/ Multiple Output (MIMO)Mobile
Channel
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Capacity of the MIMO Mobile Channel
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Coding for Mobile Channels Example of a
convolutional encoder with memory 2
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Trellis of a convolutional code with memory 2
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Circular Trellis of a Tailbiting Convolutional
Code (TBCC) with memory 2
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Tanner Graph of the Golay Code as a TBCC
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The Golay Code encoded as a TBCC of memory 4
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• Soft-in/Soft-out Decoding of CC and TB CC
• Use BCJR algorithm and ist derivatives
• (Max-Log-MAP, SOVA)
• For TBCC use WRAP around algorithm
• Problem for high rate conv. Codes
• Too many branches per state
• Solution Decode with dual code and dual
  trellis !

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Soft bits as input !
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Parallel Concatenation of Codes (Turbo Codes)
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Turbo Decoding with TBCC
Distance to Shannon 1.9 dB
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Performance of a Turbo scheme with TBCC on
Rayleigh Mobile Channel, Comp. Rate 4/5 1024
information bits
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Acknowledgement I would like to thank Dr. Elke
Offer and Dipl. Ing. Christian Weiss for
their contribution
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Information and Coding Theory for Mobile
Communications Joachim Hagenauer Summary

• Information Theory helps in the Design of Systems
  for Fading and Mimo Channels
• Taibiting Convolutional Codes (TBCC) are the Link
  between Block and Convolutional Codes
• Decoding of TBCC by the Wrap-Around APP Algorithm
• Decode High Rate TBCC by the Dualized Trellis
  where the Inputs are the Soft Bits
• Use TBCC as Component Codes in a Turbo Scheme and
  approach Capacity by less the 2 dB and for Short
  Frames
• Decode TBCC by an Analog Decoder