diff --git a/lib/fsk4hf/ldpcsim300.f90 b/lib/fsk4hf/ldpcsim300.f90
index 8fc50e564..bf1614319 100644
--- a/lib/fsk4hf/ldpcsim300.f90
+++ b/lib/fsk4hf/ldpcsim300.f90
@@ -115,7 +115,8 @@ write(*,*) i1Msg8BitBytes(1:9)
   write(*,'(38(8i1,1x))') codeword
 
 write(*,*) "Es/N0  SNR2500   ngood  nundetected nbadcrc   sigma"
-do idb = -14, 20 
+do idb = 20,-14,-1
+!do idb = -10, -10 
   db=idb/2.0-1.0
 !  sigma=1/sqrt( 2*rate*(10**(db/10.0)) )  ! to make db represent Eb/No
   sigma=1/sqrt( 2*(10**(db/10.0)) )        ! db represents Es/No
@@ -168,6 +169,9 @@ do idb = -14, 20
 
 ! max_iterations is max number of belief propagation iterations
     call bpdecode300(llr, apmask, max_iterations, decoded, niterations, cw)
+    if( niterations .lt. 0 ) then
+      call osd300(llr, decoded, niterations, cw)
+    endif
     n2err=0
     do i=1,N
       if( cw(i)*(2*codeword(i)-1.0) .lt. 0 ) n2err=n2err+1
@@ -225,7 +229,7 @@ do idb = -14, 20
       endif
     endif
   enddo
-  snr2500=db+10*log10(1.03/2500.0)
+  snr2500=db+10*log10(1.389/2500.0)
   pberr=real(nberr)/(real(ntrials*N))
   write(*,"(f4.1,4x,f5.1,1x,i8,1x,i8,1x,i8,8x,f5.2,8x,e10.3)") db,snr2500,ngood,nue,nbadcrc,ss,pberr
 
diff --git a/lib/fsk4hf/osd300.f90 b/lib/fsk4hf/osd300.f90
new file mode 100644
index 000000000..1825cd372
--- /dev/null
+++ b/lib/fsk4hf/osd300.f90
@@ -0,0 +1,409 @@
+subroutine osd300(llr,decoded,niterations,cw)
+!
+! An ordered-statistics decoder for the (300,60) code.
+! Based on the ideas in:
+! "Soft-decision decoding of linear block codes based on ordered statistics,"
+! by Marc P. C. Fossorier and Shu Lin, 
+! IEEE Trans Inf Theory, Vol 41, No 5, Sep 1995
+! 
+character*15 g(240)
+integer*1 gen(60,300)
+integer*1 genmrb(60,300)
+integer*1 temp(60),m0(60),me(60)
+integer indices(300)
+integer, parameter:: N=300, K=60, M=N-K
+integer*1 codeword(N),cw(N),apmask(N),hdec(N)
+integer  colorder(N)
+integer*1 decoded(K)
+integer indx(N),indxmrb(K)
+real llr(N),rx(N),absrx(N),absmrb(K)
+logical first
+data first/.true./
+data g/            &
+ "316fd3bb18bcefd", &
+ "a9c1c984f91244e", &
+ "9e04bd3d5d78d89", &
+ "f81617089621bd4", &
+ "12997ce2f44dbf4", &
+ "3ebddaf9b0fa1fc", &
+ "d0c114b0b0ef162", &
+ "f8c4f115f98bd92", &
+ "d0a79c0c5b8ca19", &
+ "477f6712f357b3b", &
+ "fa28b2444a7e66b", &
+ "bedcd4df8d95c64", &
+ "da30de73e57022c", &
+ "bc099bbb90fe09e", &
+ "cffc1e47e5708e8", &
+ "713d808563ca9a3", &
+ "70fcf1741d5d5d7", &
+ "32e80bc15112008", &
+ "804cef4df9b18ec", &
+ "3736881819d1033", &
+ "f4e37db7f9c5efe", &
+ "9e84b93d4d78d09", &
+ "2250c3518ec830a", &
+ "55a529a92e18021", &
+ "1cb80b14c9f6eae", &
+ "80c504b031ef926", &
+ "ece6636d0ac9c6d", &
+ "5d50a1690782cd0", &
+ "3d54a1fb30937a2", &
+ "ba8fe8006318041", &
+ "02917ce2fc45bf4", &
+ "abc1d984f95a44e", &
+ "fc05b4c4ab2d850", &
+ "467f7718f357b3b", &
+ "472cc094546c6b2", &
+ "fcdd94cf8c9cc64", &
+ "4dbc1647e970cc8", &
+ "6caa465c442aed1", &
+ "aead5af8b0da1be", &
+ "d8e1fa45a2e8431", &
+ "9d4dc4cc63abb7f", &
+ "9b2df6b48264637", &
+ "7335808563ca3a3", &
+ "36bf8d5cd93e6cc", &
+ "004ccf4db9b08ec", &
+ "90a71c8c598ca19", &
+ "f8c5d115f90bc92", &
+ "b95546c4e3f7934", &
+ "7d50a1690786cd0", &
+ "c90939921a0d7c6", &
+ "d0c504b030ef126", &
+ "ce3e6f9396fc542", &
+ "a0072a59f3707f5", &
+ "532d0a8fe3da1ea", &
+ "68b9e5cd7d142db", &
+ "fedc94df8c9dc64", &
+ "6da2465c448aed0", &
+ "3574aa19cb273c0", &
+ "1e54768c6bc6843", &
+ "691f65654498186", &
+ "fe2c92444a6ef6b", &
+ "9caad933e038cc4", &
+ "ad4e6f4defb28ec", &
+ "4f3d80947c6d2b2", &
+ "1caad933e0b8cc4", &
+ "b14fd3bf18bcafd", &
+ "ad091bbbb0f809e", &
+ "90b71c8c598da19", &
+ "f8c4d115f90bd92", &
+ "9d4dcccc63afb7f", &
+ "fa2c92444a6e76b", &
+ "1e14768c6bc6c43", &
+ "d1baf5aacb86087", &
+ "bdf762b92ee51c7", &
+ "caacec06ad8a90c", &
+ "804ccf4df9b08ec", &
+ "69e969f9da5cbd8", &
+ "814ccf4df9b086c", &
+ "cebe4f9796f4542", &
+ "491f65654499186", &
+ "8fbf5b9796f6d2a", &
+ "ce3e4f9396f4542", &
+ "47558560e7debc3", &
+ "94aadd33e038cc4", &
+ "a94eef4debb286e", &
+ "d8e5d115f91bcd2", &
+ "532d488fe3da0ab", &
+ "664e7bc4e23a80c", &
+ "94a2dd33a038cd4", &
+ "d8c5d115f91bc92", &
+ "0fef071eee60bd5", &
+ "9a89a09163c2b97", &
+ "0eaf071e6c60bd5", &
+ "bc0d1bbbb0fe0be", &
+ "f9babd3d12d0f31", &
+ "69a969f9da5c9d8", &
+ "6e4e7bc4e23a82c", &
+ "b0042659f3227f5", &
+ "2d51418f0f28347", &
+ "be0d5bbbb0da0be", &
+ "225003508ec8302", &
+ "8fbf4b9796f4d2a", &
+ "bead5af9b0da1be", &
+ "6ca2465c440aed1", &
+ "4fbc1e47ed708c8", &
+ "bd091bbbb0fc09e", &
+ "b0062259f3307f5", &
+ "a8072a59f3727f5", &
+ "a0062259f3707f5", &
+ "3c380b14c974eae", &
+ "30042659f3226f5", &
+ "48b9e4cd7d142db", &
+ "728bcd4b38308fb", &
+ "c0c504b031ef126", &
+ "314fd3bb18bcafd", &
+ "1c29148305faec1", &
+ "44c92a9c28ada63", &
+ "88e99b370aae32b", &
+ "695081690386ad8", &
+ "572d0a8de3da1ea", &
+ "467f6610f357b2b", &
+ "733d008563da1a3", &
+ "d1baf4aacb84087", &
+ "4315551d71c8ff0", &
+ "48bde4cd7d140db", &
+ "3ebd58f9b0da9fc", &
+ "51baf4aacb84083", &
+ "814e4f4de9b082c", &
+ "814ecf4de9b086c", &
+ "be0d1bbbb0fa0be", &
+ "4f7580947c792b3", &
+ "cdf2dce48c39c3b", &
+ "d8c5c115f91bc12", &
+ "a94e6f4debb28ee", &
+ "be2d5afbb0da1be", &
+ "cdd6dce48439c2b", &
+ "bebd5af9b0da1fe", &
+ "fa2892444a6e66b", &
+ "51bbf4aacb8c083", &
+ "baa73d81eebcd83", &
+ "79a2ce47f138cc9", &
+ "cc28cf198e6dbd4", &
+ "fcde94dfcc9cc64", &
+ "1016fcf59286717", &
+ "12917ce2fc4dbf4", &
+ "4fbc1647e9708c8", &
+ "3e382b1cc974fae", &
+ "d5bafdaad386087", &
+ "0fef473eee60bd5", &
+ "c0e504b031ee126", &
+ "8bbf5b9797f6d2a", &
+ "0eef071e6e60bd5", &
+ "1806fcf59386517", &
+ "fcdc94df8c9cc64", &
+ "141eca2bfa25656", &
+ "5fbc1767e9708e8", &
+ "5aa4c7803a6bdf1", &
+ "b14bd3b718bcafd", &
+ "3ebd5af9b0da1fc", &
+ "d0a7148c5b8ca09", &
+ "a94ecf4debb086e", &
+ "733d808563ca1a3", &
+ "fd9abd1d92d0f31", &
+ "bc091bbbb0fe09e", &
+ "d0c514b0b0ef122", &
+ "4f7d80947c7d2b3", &
+ "8b3f5b97b7f6d2a", &
+ "4fbc1767e9708c8", &
+ "cebf4f9796f4502", &
+ "9c76c880a864e67", &
+ "abc1c984f95244e", &
+ "795081690786ad8", &
+ "467f6710f357b3b", &
+ "1c380b14c9f4eae", &
+ "d5baf5aac386087", &
+ "bedc94df8c95c64", &
+ "553d0a8de2da1fa", &
+ "0315551d71d8ff0", &
+ "1c1eca2ffa25656", &
+ "d4bafdaad3c6087", &
+ "be2d5bfbb0da0be", &
+ "b0062659f3207f5", &
+ "5ffc1765e9708e8", &
+ "8d62e8bcd303e33", &
+ "cc08cf198e69bd4", &
+ "573d0a8de3da1fa", &
+ "cd56dce48639c2b", &
+ "472dc094546c2b2", &
+ "7950a16907868d8", &
+ "7283cf4b38308fb", &
+ "894ecf4de9b086e", &
+ "0f7580b47c792b3", &
+ "cfbf4b9796f4d0a", &
+ "3e380b14c974fae", &
+ "732d0085e3da1a3", &
+ "1816fcf59386717", &
+ "532d088fe3da1ab", &
+ "1c300b94c9fcaae", &
+ "d0a71c8c5b8ca19", &
+ "9e84bd3d5d78d09", &
+ "225083508ec830a", &
+ "f99abd1d12d0f31", &
+ "35f4aa19cb673c0", &
+ "cdd2dce48c39c2b", &
+ "0f7780b47c792bf", &
+ "0e33a5f114f5730", &
+ "bc05b4c4ab0d850", &
+ "1c300b14c9f4aae", &
+ "cfbc1e47ed708e8", &
+ "0f7180b47c392b3", &
+ "d8c7c115f91be12", &
+ "c09148adfa94e97", &
+ "9c66c880a844e67", &
+ "2226c13b73519f8", &
+ "cebf4b9796f4d02", &
+ "c0e706b031ee126", &
+ "6a6629715e53ce3", &
+ "73f9aa824e7d0b8", &
+ "473d80947c6c2b2", &
+ "1df140e0ddb5632", &
+ "473dc0945c6c2b2", &
+ "81b4d95f671971d", &
+ "663945ca758e2b6", &
+ "02ec3d98a2306fd", &
+ "5dadb0fa1275690", &
+ "4bb8aaa854948d0", &
+ "8359ba40886971c", &
+ "49cc3d2a2be2ee0", &
+ "bfdf13af137f318", &
+ "a1de773a2b1ff04", &
+ "8ff3945a2f465c7", &
+ "532d0087e3da1a3", &
+ "f3eaf7fa454d385", &
+ "a606aa5aeba07d9", &
+ "67f0627b0af8a53", &
+ "56698bed69d1c2c", &
+ "d5f420011fbf924", &
+ "2a8f86c810e2c62", &
+ "43cc1cf1208c206", &
+ "ee784c4900258de"/
+
+data colorder/    &
+0,1,2,3,4,5,6,7,8,9,10,11,123,12,13,14,15,16,17,18, &
+19,20,21,22,23,24,25,138,26,145,27,28,29,30,31,32,33,34,35,36, &
+37,154,38,39,40,41,42,43,44,144,46,47,48,49,50,51,52,53,143,54, &
+125,56,57,58,124,59,120,140,157,160,55,60,61,62,156,162,141,64,65,153, &
+181,183,66,170,67,68,69,130,70,164,71,72,73,74,75,63,76,77,135,78, &
+79,80,176,169,82,83,84,167,180,85,136,158,129,166,175,142,134,146,121,165, &
+88,89,192,90,45,91,92,93,182,189,94,95,96,173,81,97,98,178,122,126, &
+132,99,100,152,186,193,101,102,151,103,104,172,159,168,150,190,147,148,201,107, &
+205,177,108,198,197,174,127,109,185,110,202,87,199,171,179,187,139,137,106,131, &
+206,194,112,149,155,113,128,184,196,86,114,203,212,195,208,105,188,161,163,191, &
+200,209,214,204,115,218,133,111,207,117,213,216,211,217,116,215,219,220,210,221, &
+118,222,223,225,224,228,226,229,231,227,233,119,234,235,232,230,237,239,236,238, &
+240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259, &
+260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279, &
+280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299/
+
+save first,gen
+
+if( first ) then ! fill the generator matrix
+  gen=0
+  do i=1,240
+    do j=1,15
+      read(g(i)(j:j),"(Z1)") istr
+        do jj=1, 4 
+          irow=(j-1)*4+jj
+          if( btest(istr,4-jj) ) gen(irow,i)=1
+        enddo
+    enddo
+  enddo
+  do irow=1,60
+    gen(irow,240+irow)=1
+  enddo
+first=.false.
+endif
+
+! re-order received vector to place systematic msg bits at the end
+rx=llr(colorder+1) 
+
+! hard decode the received word
+hdec=0            
+where(rx .ge. 0) hdec=1
+
+! use magnitude of received symbols as a measure of reliability.
+absrx=abs(rx) 
+call indexx(absrx,N,indx)  
+! re-order the columns of the generator matrix in order of increasing reliability.
+do i=1,N
+  genmrb(1:K,N+1-i)=gen(1:K,indx(N+1-i))
+enddo
+
+! do gaussian elimination to create a generator matrix with the most reliable
+! received bits as the systematic bits. if it happens that the K most reliable
+! bits are not independent, then we will encounter a zero pivot, in that case
+! we dip into the less reliable bits to find K independent MRBs.
+! the "indices" array will track any column reordering that is done as part
+! of the gaussian elimination.
+do i=1,N
+  indices(i)=indx(i)
+enddo
+do id=1,K ! diagonal element indices 
+  do ic=id,K+20  ! 
+    icol=N-K+ic
+    if( icol .gt. N ) icol=241-(icol-300)
+    iflag=0
+    if( genmrb(id,icol) .eq. 1 ) then
+      iflag=1
+      if( icol-240 .ne. id ) then ! reorder column
+        temp(1:60)=genmrb(1:60,240+id)
+        genmrb(1:60,240+id)=genmrb(1:60,icol)
+        genmrb(1:60,icol)=temp(1:60) 
+        itmp=indices(240+id)
+        indices(240+id)=indices(icol)
+        indices(icol)=itmp
+      endif
+      do ii=1,K
+        if( ii .ne. id .and. genmrb(ii,N-K+id) .eq. 1 ) then
+          genmrb(ii,1:N)=mod(genmrb(ii,1:N)+genmrb(id,1:N),2)
+        endif
+      enddo
+      exit
+    endif
+  enddo
+enddo
+
+! now, use the indices of the K MRB bits to find the hard-decisions
+! for those bits. the resulting message is encoded to find the 
+! zero'th order codeword estimate (assuming no errors in the MRB).
+m0=0
+where (rx(indices(241:300)).ge.0.0) m0=1
+absmrb=abs(rx(indices(241:300)))
+!do i=1,60
+!write(*,*) i,absmrb(i)
+!enddo
+call indexx(absmrb,K,indxmrb)
+!do i=1,60
+!write(*,*) i,absmrb(i),indxmrb(i),absmrb(indxmrb(i))
+!enddo
+xmed=absmrb(45)
+
+! the MRB should have only a few errors. Try various error patterns,
+! re-encode each errored version of the MRBs, re-order the resulting codeword
+! and compare with the original received vector. Keep the best codeword.
+nhardmin=300
+corrmax=-1.0e32
+do i1=0,60
+  do i2=i1,60
+    do i3=i2,60
+      do i4=i3,60
+        me=m0
+        if( i1 .ne. 0 ) me(i1)=1-me(i1)
+        if( i2 .ne. 0 ) me(i2)=1-me(i2)
+        if( i3 .ne. 0 ) me(i3)=1-me(i3)
+        if( i4 .ne. 0 ) me(i4)=1-me(i4)
+
+! me is the "errored" message = MRB's + error pattern
+        do i=1, 300
+          nsum=sum(iand(me,genmrb(1:60,i)))
+          codeword(i)=mod(nsum,2)
+        enddo
+! undo the index permutations to put the "real" message bits at the end
+        codeword(indices)=codeword
+        nhard=count(codeword .ne. hdec)
+!        corr=sum(codeword*rx)  ! to save time use nhard to pick best codeword
+        if( nhard .lt. nhardmin ) then
+!         if( corr .gt. corrmax ) then
+          cw=codeword
+          nhardmin=nhard
+!          corrmax=corr
+          i1min=i1
+          i2min=i2
+          i3min=i3
+          i4min=i4
+          if( nhardmin .le. 85 ) goto 200 ! early stopping criterion
+        endif
+      enddo
+    enddo
+  enddo 
+enddo
+
+200 decoded=cw(241:300)
+!write(*,*) absmrb(i1min),absmrb(i2min),absmrb(i3min),absmrb(i4min),xmed,nhardmin
+niterations=-1
+if( nhardmin .le. 90 ) niterations=1
+return
+end subroutine osd300