mirror of https://github.com/saitohirga/WSJT-X.git
A few more minor tweaks to the text.
git-svn-id: svn+ssh://svn.code.sf.net/p/wsjt/wsjt/branches/wsjtx@6208 ab8295b8-cf94-4d9e-aec4-7959e3be5d79
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@ -118,7 +118,7 @@ on with a Reed-Solomon code.
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, the number of message symbols conveyed by the codeword, and the number
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of possible values for each symbol in the codewords.
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The codeword length and the number of message symbols are specified using
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The codeword length and the number of message symbols are specified with
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the notation
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\begin_inset Formula $(n,k)$
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\end_inset
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@ -252,7 +252,11 @@ errors.
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\begin_inset Formula $s$
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\end_inset
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symbols are erased and the remaining (unerased) symbols contain
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symbols are erased and the remaining
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\begin_inset Formula $n-s$
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\end_inset
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symbols contain
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\begin_inset Formula $e$
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\end_inset
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@ -296,7 +300,7 @@ errors-and-erasures
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decoder.
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The possibility of doing errors-and-erasures decoding lies at the heart
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of the FT algorithm.
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On that foundation we build a capability for using
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On that foundation we have built a capability for using
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\begin_inset Quotes eld
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\end_inset
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@ -304,7 +308,7 @@ soft
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\begin_inset Quotes erd
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\end_inset
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information on symbol reliability.
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information on symbol reliability, thereby producing a soft-decision decoder.
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\end_layout
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\begin_layout Section
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@ -319,7 +323,7 @@ Do I feel lucky?
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\begin_layout Standard
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The FT algorithm uses the estimated quality of received symbols to generate
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lists of symbols considered likely to be in error, thereby enabling reliable
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lists of symbols considered likely to be in error, thus enabling reliable
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decoding of received words with more than 25 errors.
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As a specific example, consider a received JT65 word with 23 correct symbols
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and 40 errors.
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@ -559,8 +563,8 @@ Examples 1 and 2 show that a random strategy for selecting symbols to erase
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incorrect symbols, as before, but suppose we know that 10 symbols are significa
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ntly more reliable than the other 53.
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We might therefore protect the 10 most reliable symbols from erasure, and
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choose erasures from the smaller set of
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We might therefore protect the 10 most reliable symbols from erasure, selecting
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erasures from the smaller set of
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\begin_inset Formula $N=53$
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\end_inset
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@ -641,8 +645,8 @@ The FT decoding algorithm
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Example 3 shows how reliable information about symbol quality should make
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it possible to decode received frames having a large number of errors.
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In practice the number of errors in the received word is unknown, so we
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use a stochastic algorithm to assign a high erasure probability to low-quality
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symbols and a relatively low probability to high-quality symbols.
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use a stochastic algorithm to assign high erasure probability to low-quality
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symbols and relatively low probability to high-quality symbols.
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As illustrated by Example 3, a good choice of these probabilities can increase
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the chance of a successful decode by many orders of magnitude.
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\end_layout
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@ -651,8 +655,7 @@ Example 3 shows how reliable information about symbol quality should make
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The FT algorithm uses two quality indices made available by a noncoherent
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64-FSK demodulator.
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The demodulator identifies the most likely value for each symbol based
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on which of 64 frequency bins contains the the largest signal-plus-noise
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power.
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on the largest signal-plus-noise power in 64 frequency bins.
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The fraction of total power in the two bins containing the largest and
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second-largest powers (denoted by
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\begin_inset Formula $p_{1}$
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@ -931,6 +934,9 @@ Number of decodes vs.
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\begin_layout Itemize
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Probability of successful decode vs.
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Es/No or S/N in 2500 Hz BW
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\end_layout
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\begin_layout Standard
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\begin_inset Float figure
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wide false
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sideways false
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@ -940,6 +946,8 @@ status open
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\align center
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\begin_inset Graphics
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filename fig_psuccess.pdf
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lyxscale 120
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scale 120
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\end_inset
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@ -952,8 +960,8 @@ status open
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\begin_layout Plain Layout
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Percentage of JT65 messages successfully decoded as a function of SNR in
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2.5 kHz bandwidth.
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Results are shown for the hard-decision Berlekamp-Massey (BM) and the Franke-Ta
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ylor (FT) decoding algorithms.
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Results are shown for the hard-decision Berlekamp-Massey (BM) and the sofft-dec
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ision Franke-Taylor (FT) decoding algorithms.
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\end_layout
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\end_inset
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