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git-svn-id: svn+ssh://svn.code.sf.net/p/wsjt/wsjt/branches/wsjtx@7662 ab8295b8-cf94-4d9e-aec4-7959e3be5d79
410 lines
10 KiB
Fortran
410 lines
10 KiB
Fortran
subroutine osd300(llr,decoded,niterations,cw)
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!
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! An ordered-statistics decoder for the (300,60) code.
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! Based on the ideas in:
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! "Soft-decision decoding of linear block codes based on ordered statistics,"
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! by Marc P. C. Fossorier and Shu Lin,
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! IEEE Trans Inf Theory, Vol 41, No 5, Sep 1995
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!
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character*15 g(240)
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integer*1 gen(60,300)
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integer*1 genmrb(60,300)
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integer*1 temp(60),m0(60),me(60)
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integer indices(300)
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integer, parameter:: N=300, K=60, M=N-K
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integer*1 codeword(N),cw(N),apmask(N),hdec(N)
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integer colorder(N)
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integer*1 decoded(K)
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integer indx(N),indxmrb(K)
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real llr(N),rx(N),absrx(N),absmrb(K)
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logical first
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data first/.true./
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data g/ &
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"316fd3bb18bcefd", &
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"a9c1c984f91244e", &
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"9e04bd3d5d78d89", &
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"f81617089621bd4", &
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"12997ce2f44dbf4", &
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"3ebddaf9b0fa1fc", &
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"d0c114b0b0ef162", &
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"f8c4f115f98bd92", &
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"d0a79c0c5b8ca19", &
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"477f6712f357b3b", &
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"fa28b2444a7e66b", &
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"bedcd4df8d95c64", &
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"da30de73e57022c", &
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"bc099bbb90fe09e", &
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"cffc1e47e5708e8", &
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"713d808563ca9a3", &
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"70fcf1741d5d5d7", &
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"32e80bc15112008", &
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"804cef4df9b18ec", &
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"3736881819d1033", &
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"f4e37db7f9c5efe", &
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"9e84b93d4d78d09", &
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"2250c3518ec830a", &
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"55a529a92e18021", &
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"1cb80b14c9f6eae", &
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"80c504b031ef926", &
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"ece6636d0ac9c6d", &
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"5d50a1690782cd0", &
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"3d54a1fb30937a2", &
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"ba8fe8006318041", &
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"02917ce2fc45bf4", &
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"abc1d984f95a44e", &
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"fc05b4c4ab2d850", &
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"467f7718f357b3b", &
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"472cc094546c6b2", &
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"fcdd94cf8c9cc64", &
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"4dbc1647e970cc8", &
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"6caa465c442aed1", &
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"aead5af8b0da1be", &
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"d8e1fa45a2e8431", &
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"9d4dc4cc63abb7f", &
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"9b2df6b48264637", &
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"7335808563ca3a3", &
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"36bf8d5cd93e6cc", &
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"004ccf4db9b08ec", &
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"90a71c8c598ca19", &
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"f8c5d115f90bc92", &
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"b95546c4e3f7934", &
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"7d50a1690786cd0", &
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"c90939921a0d7c6", &
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"d0c504b030ef126", &
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"ce3e6f9396fc542", &
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"a0072a59f3707f5", &
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"532d0a8fe3da1ea", &
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"68b9e5cd7d142db", &
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"fedc94df8c9dc64", &
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"6da2465c448aed0", &
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"3574aa19cb273c0", &
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"1e54768c6bc6843", &
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"691f65654498186", &
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"fe2c92444a6ef6b", &
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"9caad933e038cc4", &
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"ad4e6f4defb28ec", &
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"4f3d80947c6d2b2", &
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"1caad933e0b8cc4", &
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"b14fd3bf18bcafd", &
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"ad091bbbb0f809e", &
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"90b71c8c598da19", &
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"f8c4d115f90bd92", &
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"9d4dcccc63afb7f", &
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"fa2c92444a6e76b", &
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"1e14768c6bc6c43", &
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"d1baf5aacb86087", &
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"bdf762b92ee51c7", &
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"caacec06ad8a90c", &
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"804ccf4df9b08ec", &
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"69e969f9da5cbd8", &
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"814ccf4df9b086c", &
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"cebe4f9796f4542", &
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"491f65654499186", &
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"8fbf5b9796f6d2a", &
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"ce3e4f9396f4542", &
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"47558560e7debc3", &
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"94aadd33e038cc4", &
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"a94eef4debb286e", &
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"d8e5d115f91bcd2", &
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"532d488fe3da0ab", &
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"664e7bc4e23a80c", &
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"94a2dd33a038cd4", &
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"d8c5d115f91bc92", &
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"0fef071eee60bd5", &
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"9a89a09163c2b97", &
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"0eaf071e6c60bd5", &
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"bc0d1bbbb0fe0be", &
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"f9babd3d12d0f31", &
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"69a969f9da5c9d8", &
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"6e4e7bc4e23a82c", &
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"b0042659f3227f5", &
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"2d51418f0f28347", &
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"be0d5bbbb0da0be", &
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"225003508ec8302", &
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"8fbf4b9796f4d2a", &
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"bead5af9b0da1be", &
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"6ca2465c440aed1", &
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"4fbc1e47ed708c8", &
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"bd091bbbb0fc09e", &
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"b0062259f3307f5", &
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"a8072a59f3727f5", &
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"a0062259f3707f5", &
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"3c380b14c974eae", &
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"30042659f3226f5", &
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"48b9e4cd7d142db", &
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"728bcd4b38308fb", &
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"c0c504b031ef126", &
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"314fd3bb18bcafd", &
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"1c29148305faec1", &
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"44c92a9c28ada63", &
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"88e99b370aae32b", &
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"695081690386ad8", &
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"572d0a8de3da1ea", &
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"467f6610f357b2b", &
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"733d008563da1a3", &
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"d1baf4aacb84087", &
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"4315551d71c8ff0", &
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"48bde4cd7d140db", &
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"3ebd58f9b0da9fc", &
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"51baf4aacb84083", &
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"814e4f4de9b082c", &
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"814ecf4de9b086c", &
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"be0d1bbbb0fa0be", &
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"4f7580947c792b3", &
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"cdf2dce48c39c3b", &
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"d8c5c115f91bc12", &
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"a94e6f4debb28ee", &
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"be2d5afbb0da1be", &
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"cdd6dce48439c2b", &
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"bebd5af9b0da1fe", &
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"fa2892444a6e66b", &
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"51bbf4aacb8c083", &
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"baa73d81eebcd83", &
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"79a2ce47f138cc9", &
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"cc28cf198e6dbd4", &
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"fcde94dfcc9cc64", &
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"1016fcf59286717", &
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"12917ce2fc4dbf4", &
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"4fbc1647e9708c8", &
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"3e382b1cc974fae", &
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"d5bafdaad386087", &
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"0fef473eee60bd5", &
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"c0e504b031ee126", &
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"8bbf5b9797f6d2a", &
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"0eef071e6e60bd5", &
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"1806fcf59386517", &
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"fcdc94df8c9cc64", &
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"141eca2bfa25656", &
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"5fbc1767e9708e8", &
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"5aa4c7803a6bdf1", &
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"b14bd3b718bcafd", &
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"3ebd5af9b0da1fc", &
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"d0a7148c5b8ca09", &
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"a94ecf4debb086e", &
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"733d808563ca1a3", &
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"fd9abd1d92d0f31", &
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"bc091bbbb0fe09e", &
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"d0c514b0b0ef122", &
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"4f7d80947c7d2b3", &
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"8b3f5b97b7f6d2a", &
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"4fbc1767e9708c8", &
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"cebf4f9796f4502", &
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"9c76c880a864e67", &
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"abc1c984f95244e", &
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"795081690786ad8", &
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"467f6710f357b3b", &
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"1c380b14c9f4eae", &
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"d5baf5aac386087", &
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"bedc94df8c95c64", &
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"553d0a8de2da1fa", &
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"0315551d71d8ff0", &
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"1c1eca2ffa25656", &
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"d4bafdaad3c6087", &
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"be2d5bfbb0da0be", &
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"b0062659f3207f5", &
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"5ffc1765e9708e8", &
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"8d62e8bcd303e33", &
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"cc08cf198e69bd4", &
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"573d0a8de3da1fa", &
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"cd56dce48639c2b", &
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"472dc094546c2b2", &
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"7950a16907868d8", &
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"7283cf4b38308fb", &
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"894ecf4de9b086e", &
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"0f7580b47c792b3", &
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"cfbf4b9796f4d0a", &
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"3e380b14c974fae", &
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"732d0085e3da1a3", &
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"1816fcf59386717", &
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"532d088fe3da1ab", &
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"1c300b94c9fcaae", &
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"d0a71c8c5b8ca19", &
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"9e84bd3d5d78d09", &
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"225083508ec830a", &
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"f99abd1d12d0f31", &
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"35f4aa19cb673c0", &
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"cdd2dce48c39c2b", &
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"0f7780b47c792bf", &
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"0e33a5f114f5730", &
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"bc05b4c4ab0d850", &
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"1c300b14c9f4aae", &
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"cfbc1e47ed708e8", &
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"0f7180b47c392b3", &
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"d8c7c115f91be12", &
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"c09148adfa94e97", &
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"9c66c880a844e67", &
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"2226c13b73519f8", &
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"cebf4b9796f4d02", &
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"c0e706b031ee126", &
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"6a6629715e53ce3", &
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"73f9aa824e7d0b8", &
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"473d80947c6c2b2", &
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"1df140e0ddb5632", &
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"473dc0945c6c2b2", &
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"81b4d95f671971d", &
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"663945ca758e2b6", &
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"02ec3d98a2306fd", &
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"5dadb0fa1275690", &
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"4bb8aaa854948d0", &
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"8359ba40886971c", &
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"49cc3d2a2be2ee0", &
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"bfdf13af137f318", &
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"a1de773a2b1ff04", &
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"8ff3945a2f465c7", &
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"532d0087e3da1a3", &
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"f3eaf7fa454d385", &
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"a606aa5aeba07d9", &
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"67f0627b0af8a53", &
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"56698bed69d1c2c", &
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"d5f420011fbf924", &
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"2a8f86c810e2c62", &
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"43cc1cf1208c206", &
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"ee784c4900258de"/
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data colorder/ &
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0,1,2,3,4,5,6,7,8,9,10,11,123,12,13,14,15,16,17,18, &
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19,20,21,22,23,24,25,138,26,145,27,28,29,30,31,32,33,34,35,36, &
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37,154,38,39,40,41,42,43,44,144,46,47,48,49,50,51,52,53,143,54, &
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125,56,57,58,124,59,120,140,157,160,55,60,61,62,156,162,141,64,65,153, &
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181,183,66,170,67,68,69,130,70,164,71,72,73,74,75,63,76,77,135,78, &
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79,80,176,169,82,83,84,167,180,85,136,158,129,166,175,142,134,146,121,165, &
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88,89,192,90,45,91,92,93,182,189,94,95,96,173,81,97,98,178,122,126, &
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132,99,100,152,186,193,101,102,151,103,104,172,159,168,150,190,147,148,201,107, &
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205,177,108,198,197,174,127,109,185,110,202,87,199,171,179,187,139,137,106,131, &
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206,194,112,149,155,113,128,184,196,86,114,203,212,195,208,105,188,161,163,191, &
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200,209,214,204,115,218,133,111,207,117,213,216,211,217,116,215,219,220,210,221, &
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118,222,223,225,224,228,226,229,231,227,233,119,234,235,232,230,237,239,236,238, &
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240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259, &
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260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279, &
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280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299/
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save first,gen
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if( first ) then ! fill the generator matrix
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gen=0
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do i=1,240
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do j=1,15
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read(g(i)(j:j),"(Z1)") istr
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do jj=1, 4
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irow=(j-1)*4+jj
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if( btest(istr,4-jj) ) gen(irow,i)=1
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enddo
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enddo
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enddo
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do irow=1,60
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gen(irow,240+irow)=1
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enddo
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first=.false.
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endif
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! re-order received vector to place systematic msg bits at the end
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rx=llr(colorder+1)
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! hard decode the received word
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hdec=0
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where(rx .ge. 0) hdec=1
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! use magnitude of received symbols as a measure of reliability.
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absrx=abs(rx)
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call indexx(absrx,N,indx)
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! re-order the columns of the generator matrix in order of increasing reliability.
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do i=1,N
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genmrb(1:K,N+1-i)=gen(1:K,indx(N+1-i))
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enddo
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! do gaussian elimination to create a generator matrix with the most reliable
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! received bits as the systematic bits. if it happens that the K most reliable
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! bits are not independent, then we will encounter a zero pivot, in that case
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! we dip into the less reliable bits to find K independent MRBs.
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! the "indices" array will track any column reordering that is done as part
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! of the gaussian elimination.
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do i=1,N
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indices(i)=indx(i)
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enddo
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do id=1,K ! diagonal element indices
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do ic=id,K+20 !
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icol=N-K+ic
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if( icol .gt. N ) icol=241-(icol-300)
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iflag=0
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if( genmrb(id,icol) .eq. 1 ) then
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iflag=1
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if( icol-240 .ne. id ) then ! reorder column
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temp(1:60)=genmrb(1:60,240+id)
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genmrb(1:60,240+id)=genmrb(1:60,icol)
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genmrb(1:60,icol)=temp(1:60)
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itmp=indices(240+id)
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indices(240+id)=indices(icol)
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indices(icol)=itmp
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endif
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do ii=1,K
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if( ii .ne. id .and. genmrb(ii,N-K+id) .eq. 1 ) then
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genmrb(ii,1:N)=mod(genmrb(ii,1:N)+genmrb(id,1:N),2)
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endif
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enddo
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exit
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endif
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enddo
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enddo
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! now, use the indices of the K MRB bits to find the hard-decisions
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! for those bits. the resulting message is encoded to find the
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! zero'th order codeword estimate (assuming no errors in the MRB).
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m0=0
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where (rx(indices(241:300)).ge.0.0) m0=1
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absmrb=abs(rx(indices(241:300)))
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!do i=1,60
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!write(*,*) i,absmrb(i)
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!enddo
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call indexx(absmrb,K,indxmrb)
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!do i=1,60
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!write(*,*) i,absmrb(i),indxmrb(i),absmrb(indxmrb(i))
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!enddo
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xmed=absmrb(45)
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! the MRB should have only a few errors. Try various error patterns,
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! re-encode each errored version of the MRBs, re-order the resulting codeword
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! and compare with the original received vector. Keep the best codeword.
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nhardmin=300
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corrmax=-1.0e32
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do i1=0,60
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do i2=i1,60
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do i3=i2,60
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do i4=i3,60
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me=m0
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if( i1 .ne. 0 ) me(i1)=1-me(i1)
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if( i2 .ne. 0 ) me(i2)=1-me(i2)
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if( i3 .ne. 0 ) me(i3)=1-me(i3)
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if( i4 .ne. 0 ) me(i4)=1-me(i4)
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! me is the "errored" message = MRB's + error pattern
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do i=1, 300
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nsum=sum(iand(me,genmrb(1:60,i)))
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codeword(i)=mod(nsum,2)
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enddo
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! undo the index permutations to put the "real" message bits at the end
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codeword(indices)=codeword
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nhard=count(codeword .ne. hdec)
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! corr=sum(codeword*rx) ! to save time use nhard to pick best codeword
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if( nhard .lt. nhardmin ) then
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! if( corr .gt. corrmax ) then
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cw=codeword
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nhardmin=nhard
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! corrmax=corr
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i1min=i1
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i2min=i2
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i3min=i3
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i4min=i4
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if( nhardmin .le. 85 ) goto 200 ! early stopping criterion
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endif
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enddo
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enddo
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enddo
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enddo
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200 decoded=cw(241:300)
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!write(*,*) absmrb(i1min),absmrb(i2min),absmrb(i3min),absmrb(i4min),xmed,nhardmin
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niterations=-1
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if( nhardmin .le. 90 ) niterations=1
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return
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end subroutine osd300
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