Joint Optimization of Full-Duplex Relay Placement and Transmit Power for Multihop Ultraviolet Communications
Bibliographic record
Abstract
Ultraviolet (UV) communication is a promising technology for civilian and military secure communication systems due to nonline-of-sight transmission, low background noise, and high local security. The full-duplex relay-assisted UV communications can achieve longer communication distances and higher efficiency of time–frequency utilization compared with direct UV communications. However, due to the strong scattering effect, serious interrelay-interference (IRI) is inevitably introduced in multihop full-duplex relay links. To mitigate the impacts of IRI, this article proposes an alternate iterative-Newton method (AINM) to optimize jointly the relay placement and transmit powers of each relay. We further propose a space-division coupled full-duplex relay configuration to reduce the influence of IRI. Numerical results show that the proposed AINM can significantly decrease the bit-error rate (BER); and the proposed space-division scheme can further decrease the BER but sacrifices about half achievable information rate. Besides, we demonstrate that, when the communication links are strong, the distance between adjacent relays should gradually decrease and the transmit power increase from the source node to the destination node. However, when the communication links are weak, each relay should adopt its maximum transmit power to achieve the minimum BER.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".