Raptor Code based on punctured LDPC for Secrecy in Massive MiMo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it