Optimal power allocation to improve secrecy performance of non‐regenerative cooperative systems using an untrusted relay
Bibliographic record
Abstract
To protect data communication from eavesdropper nodes, different techniques have been developed to improve the physical‐layer security (PLS) of communications systems. Destination‐based cooperative signalling (DBCS) is one of such techniques where the destination sends an intended artificial noise to the untrusted listeners helping to protect the source message from being captured reliably at eavesdroppers. In this study, the authors investigate the application of DBCS to improve the PLS, and as a consequence the secrecy performance of a two‐hop amplify‐and‐forward cooperative system with an untrusted relay. To get the best performance out of DBCS, the transmit power of the source's signal as well as the artificial noise should be carefully adjusted. To address this, they have introduced the optimal power allocation to maximise the secrecy rate of the system under a sum‐power constraint at the network nodes. For a system with large‐scale antenna arrays at the base station, then then find the closed‐form solution for the secrecy outage probability and the ergodic secrecy rate of the optimised system for both uplink and downlink. The presented simulation results validate the authors’ theoretical analysis and reveal that the proposed DBCS with optimal power allocation significantly improves the secrecy performance of the system.
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".