TAS-Based Incremental Hybrid Decode–Amplify–Forward Relaying for Physical Layer Security Enhancement
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 this paper, a transmit antenna selection (TAS)-based incremental hybrid decode-amplify-forward (IHDAF) scheme is proposed to enhance physical layer security in cooperative relay networks. Specifically, TAS is adopted at the source in order to reduce the feedback overhead. In the proposed TAS-based IHDAF scheme, the network transmits signals to the destination adaptive select direction transmission (DT) mode, AF mode, or DF mode depending on the capacity of the source-relay link and source-relay link. In order to fully examine the benefits of the proposed TAS-based IHDAF scheme, we first derive its secrecy outage probability (SOP) in a closed-form expression. We then conduct asymptotic analysis on the SOP, which reveals the secrecy performance floor of the proposed TAS-based IHDAF scheme when no channel state information is available at the source. Theoretical analysis and simulation results demonstrate that the proposed TAS-based IHDAF scheme outperforms the selective decode-and-forward, the incremental decode-and-forward, and the noncooperative DT schemes in terms of the SOP and effective secrecy throughout, especially when the relay is close to the destination. Furthermore, the proposed TAS-based IHDAF scheme offer a good tradeoff between complexity and performance compared with using all antennas at the source.
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 | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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