On GRAND-Assisted Vector Random Linear Network Coding in Wireless Broadcasts
Rina Su, Chengji Zhao, Qifu Sun, Linqi Song · 2026
Read On GRAND-Assisted Vector Random Linear Network Coding in Wireless Broadcasts by Rina Su, Chengji Zhao, Qifu Sun, Linqi Song (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13114364.txt.
Description
Rina Su and co-authors improve wireless broadcast coding with an error-guessing decoder in a 2026 paper. Broadcasters can mix packets with random linear codes so receivers rebuild missing data, and a guessing decoder can fix some bit errors before the rebuild. Past schemes worked only with the simpler scalar form of the coding.
The team set out the general framework for the richer vector form and gave a design rule that yields error estimates at no extra computing cost. They state exactly when those estimates are right. Two concrete schemes follow. One, based on matrix form, matches the scalar delay while cutting computing by about a third. A sparser second scheme trims more computing for a slight delay cost.
Catalog record
| CREATOR | Rina Su, Chengji Zhao, Qifu Sun, Linqi Song |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 46 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |