Raptor Code based on punctured LDPC for Secrecy in Massive MiMo
Bibliographic record
Abstract
In the future Fifth-Generation networks, the eavesdropping is a critical threat due to their broadcast-based transmission. This problem can be addressed with the cryptographic protocols. However, this method is complex and difficult because of the dynamic topology of wireless networks, which does not allow an efficient management of security keys. As a complement solution, Physical-layer security (PLS) is integrated to enhance secrecy in wireless networks. The PLS exploits the schemes features of this layer, namely the modulation, Massive Multi-Input Multi-Output(m-MiMo) and channel coding. The fountain code is one of those systems where the secrecy is provided when the destination retrieves packets encoded before the intruder. Nevertheless, the secrecy can not be guaranteed when eavesdropper uses large number of the antennas as in the m-MiMo. The feature of m-MiMo should be considered to secure main channel with fountain codes. Therefore, we propose to use Raptor code which is a class of fountain code, aided by an Artificial noise (AN) and the punctuated data to reduce the efficient of intruder channel. This allows the main channel to retrieve the signal before eavesdropper. The numerical results show that using Raptor code in massive MiMo enhances the reliability and the security on the channel of legitimate user, while minimizes the abilities of intruders to spy on data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".