Reduced-complexity syndrome-based TTCM decoding
Reduced-complexity syndrome-based TTCM decoding
The iterative decoder of Turbo Trellis Coded Modulation (TTCM) exchanges extrinsic information between the constituent TCM decoders, which imposes a high computational complexity at the receiver. Therefore we conceive the syndrome-based block decoding of TTCM, which is capable of reducing the decoding complexity by disabling the decoder, when syndrome becomes zero. Quantitatively, we demonstrate that a decoding complexity reduction of at least 17% is attained at high SNRs, with at least 20% and 45% reduction in the 5th and 6th iterations, respectively.
1220-7798
Babar, Zunaira
23ede793-1796-449d-b5aa-93a297e5677a
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
June 2013
Babar, Zunaira
23ede793-1796-449d-b5aa-93a297e5677a
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Babar, Zunaira, Ng, Soon Xin and Hanzo, Lajos
(2013)
Reduced-complexity syndrome-based TTCM decoding.
IEEE Communications Letters, 17 (6), .
(doi:10.1109/LCOMM.2013.050313.130182).
Abstract
The iterative decoder of Turbo Trellis Coded Modulation (TTCM) exchanges extrinsic information between the constituent TCM decoders, which imposes a high computational complexity at the receiver. Therefore we conceive the syndrome-based block decoding of TTCM, which is capable of reducing the decoding complexity by disabling the decoder, when syndrome becomes zero. Quantitatively, we demonstrate that a decoding complexity reduction of at least 17% is attained at high SNRs, with at least 20% and 45% reduction in the 5th and 6th iterations, respectively.
Text
CL2013-0182.pdf
- Accepted Manuscript
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e-pub ahead of print date: 13 May 2013
Published date: June 2013
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 352117
URI: http://eprints.soton.ac.uk/id/eprint/352117
PURE UUID: c34cb6ee-e51b-48fc-bf87-d01b94900564
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Date deposited: 02 May 2013 10:44
Last modified: 18 Mar 2024 03:23
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Author:
Zunaira Babar
Author:
Soon Xin Ng
Author:
Lajos Hanzo
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