Soft output decoder for convolutional codes

Details Soft output decoder for convolutional codes Soft output decoder for convolutional codes

2000-09-06

2002-09-17

Pham, Chi (Department: 2631)

Pulse or digital communications

Systems using alternating or pulsating current

Plural channels for transmission of a single pulse train

C375S341000, C714S795000

Reexamination Certificate

active

06452979

ABSTRACT:

FIELD OF THE INVENTION
This invention relates generally to communication systems, and more particularly to soft output decoders for use in a receiver of a convolutional code communication system.
BACKGROUND OF THE INVENTION
Convolutional codes are often used in digital communication systems to protect transmitted information. At the transmitter, an outgoing code vector may be described using a trellis diagram whose complexity is determined by the constraint length of the encoder. Although computational complexity increases with increasing constraint length, the robustness of the coding also increases with constraint length.
At the receiver, a practical soft-decision decoder, such as a Viterbi decoder as is known in the art, uses a trellis structure to perform an optimum search for the maximum likelihood transmitted code vector. Coders for various communications systems, such as Direct Sequence Code Division Multiple Access (DS-CDMA) standard IS-95 and Global System for Mobile Communications (GSM) employ convolutional codes for error correction.
More recently, turbo codes have been developed that outperform conventional coding techniques. Turbo codes are generally composed of two or more convolutional codes and turbo interleavers. Turbo decoding is iterative using a soft output decoder to decode the constituent convolutional codes. The soft output decoder provides a reliability measure on each information bit, which helps the soft output decoder decode the other convolutional codes. The soft output decoder is usually a MAP (maximum a posterior) decoder, which uses both backward and forward decoding to determine the soft output. However, because of memory, processing, and numerical tradeoffs, MAP decoding is usually limited to a sub-optimal approximation. All of these variants require both forward and backward decoding over the block.
For future standards, such as the 3GPP (third generation partnership project for wireless systems), an 8-state turbo code with a block length of N=5120, will need 40960 words of intermediate storage, which may be unacceptable for practical implementation. Future systems employing larger frames and a greater number of states will require even more memory. By comparison, a Viterbi decoder that does not produce soft outputs for an N=5120, 8-state trellis requires less than 100 words of intermediate storage.
There is a need for a soft output decoder that reduces overall memory and processing requirements for decoding convolutional codes without the limitations imposed by prior art turbo and MAP decoders.


REFERENCES:
patent: 5933462 (1999-08-01), Viterbi et al.
patent: 1 115 209 (2001-07-01), None
Meir Ariel and Jakov Snyders, “Error-Trellises for Convolutional Codes—Part I: Construction”, IEEE Transactions on Communications, vol. 46, No. 12, Dec. 1998, 10 pages.
Meir Ariel and Jakov Snyders, “Error-Trellises for Convolutional Codes—Part II: Decoding Methods”, IEEE Transactions on Communications, vol. 47, No. 7, Jul. 1999, 10 pages.
L.R. Bahl, J. Cocke, F. Jelinek and J. Raviv, “Optimal Decoding of Linear Codes for Minimizing Symbol Error Rate”, IEEE Transactions on Information Theory, Mar. 1974, 4 pages.
Jachim Hagenauer and Peter Hoeher, “A Viterbi Algorithm with Soft-Decision Outputs and its Applications”, IEEE 1989, 7 pages.
Claude Berrou, Alain Glavieux and Puny Thitimajshima, “Near Shannon Limit Error—Correcting Coding and Decoding: Turbo-Codes (1)”, IEEE 1993, 7 pages.
Patrick Robertson, Emmanuelle Villebrun and Peter Hoeher, “A Comparison of Optimal and Sub-Optimal MAP Decoding Algorithms Operating in the Log Domain”, IEEE 1995, 5 pages.

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