Optimal power allocation in serial relay‐assisted underwater wireless optical communication systems
Bibliographic record
Abstract
Abstract For a serial relaying underwater wireless optical communication (UWOC) system with ON‐OFF keying modulation, we theoretically evaluate the optimal power allocation techniques in order to minimise the end‐to‐end bit error rate (BER), subject to transmission power constraints. At first, we evaluate the end‐to‐end BER with respect to all degrading effects of the UWOC channel, namely scattering, absorption, and turbulence‐induced fading and then develop a closed‐form BER expression as a function of transceiver parameters and water type. The optimal power allocation methods are obtained using the perfect channel state information available at the receiver (CSIR) and transmitter (CSIT) for both detect‐and‐forward (DF) and amplify‐and‐forward (AF) serial relaying systems. For each relaying method, we consider a dual‐hop UWOC system and determine optimal power allocation to minimise the BER. Afterwards, the optimal power allocation in a multi‐hop system is obtained to minimise the end‐to‐end BER. Compared to the equal power allocation, our results illustrate that UWOC relaying systems with optimal power allocation can significantly improve the end‐to‐end BER and expand the communication link. For instance, the proposed power allocation method for the DF and AF relay node in a 60 m single relay system improves the system performance at the BER of 10 −5 by 2.5 and 1.8 dB compared to the equal power allocation, respectively.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".