LDPC and ultra-massive multi-input multi-output for secrecy in 6G
Bibliographic record
Abstract
Sixth generation (6G) wireless networks are a promising technology for meeting the extreme demands of 2030 of high data rates, ultra-low latency, and ultra-reliability. However, supporting the services of 6G networks requires advanced security mechanisms. In addition, 6G has a rapid and simple means of accessing the channel because of its broadcast transmission method, which makes it prone to eavesdropping attacks. Physical-layer security (PLS) is predicted to be a suitable method of achieving network security, in which the characteristics and advantages of wireless networks are exploited. PLS technologies include wiretap code designs, artificial noise injection (jamming), multi-input multi-output (MIMO)(beamforming), and physical-layer authentication. This paper proposes an erasure corrector code, namely a low-density parity check (LDPC), and ultra-massive MIMO (UMMIMO) to fix this issue. The simulation results show that our solution guarantees data secrecy, even against eavesdroppers with many antennas and considerable decoding resources, while reducing power consumption, consequently supporting secure, green transmission.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".