The interleaver for Opulent Voice needs to be updated because the frame size has increased. We are incorporating RTP, UDP, and IP layers into the existing OPUS and 4-ary MFSK layers and now have what we think may be the final frame size.
Since convolutional encoding is used for Opulent Voice payload, an interleaver is necessary to get the best bit error rate performance out of the convolutional encoder. The interleaver is used over both the physical layer header (Golay encoded) and the data payload (a 1/2 rate Convolutional code). Opulent Voice is an open protocol that we use for our HEO/GEO uplink. It can also be used terrestrially on the #hamradio bands at 70cm and above. Find out more at https://www.openresearch.institute/2022/07/30/opulent-voice-digital-voice-and-data-protocol-update/
The distance that an interleaver spreads out bits in a frame is the most familiar performance measurement. It’s commonly called “spread” or “minimum interleaved distance”. However, we learned about another metric that is important in Turbo codes. Several papers refer to the measure of randomness of the mixture of bit position reassignments as “dispersion” (for example, see https://cgi.tu-harburg.de/~c00e8lit/cgi-bin/ivpub/download.php?file=kb-wpmc2008.pdf). That particular paper cited another paper (reference [6]) as defining dispersion.
Following that citation lead to a paper but this paper didn’t mention dispersion or explain the equation. Going back to the original paper, we started working with the definition for dispersion that we had. This used the cardinality of the set of indices of original bit positions vs. permuted bit positions. This seemed straightforward enough. But, after trying this in MATLAB, we always got the minimum dispersion value, so there must be something wrong with our interpretation.
Volunteers then spent time trying to figure out if dispersion is important enough metric for a single convolutional code, like we have in #OpulentVoice. In other words, should we simply not simply choose the polynomials that result in the largest minimum interleaved distance? Selecting the right interleaver based on a balance between how far apart it spreads the bits vs. how randomly the bits are distributed is a useful selection methodology for Turbo codes, but may not be strictly necessary for a single convolutional code used with 40 mS frames.
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