Invited Paper: Raptor Code and Massive MiMo for Secure Wireless Delivery in 5G
Bibliographic record
Abstract
Based on broadcast transmission, the future Fifth-Generation networks, 5G, suffer from a critical threat, which is the eavesdropping. This issue can be fixed with the cryptographic protocols. Nevertheless, this method is complex and challenging because of the active topology of wireless networks, which does not permit effective management of security keys. Recently, Physical Layer Security (PLS) method is applied as an alternative solution to mitigate the privacy problem, where the characteristic of the physical layer schemes, namely the modulation, Massive Multi-Input Multi-Output (m-MiMo) and channel coding are exploited to ensure privacy. The fountain code is one of these methods where the legitimate receiver must recover the message before eavesdropper did. However, this feature cannot be exploited in 5G networks in the presence of an intruder using the m-MiMo. Furthermore, the design of Artificial Noise (AN) needed in m-MiMo involves a computational complexity and excessive consumption of energy that complicate the secrecy management for fountain code. In this article, we propose a new method to avoid this problem by judiciously exploiting the features of both technologies. The new approach uses the Raptor code feature, as considerably as the m-MiMo parameters aided by AN signal while reducing the transmission power of the AN. The numerical results indicate that the new approach ensures the protection of legitimate users on the channel and minimizes energy expenditure, which potentially gets to this proposed method a greener and secure transmission.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".