Optimal Power Allocation for Multiuser Photon-Counting Underwater Optical Wireless Communications Under Poisson Shot Noise
Bibliographic record
Abstract
Photon counting is an effective technique to detect low-power optical signals in underwater optical wireless communications (UOWC), but undergoes signal-dependent Poisson shot noises that lead to intractable data rate expressions and hinder effective power allocation of photon-counting systems. This paper presents a new approach to the optimal power allocation of a multiuser photon-counting UOWC system, where we first derive the asymptotic achievable rate as the background radiation is large under the signal-dependent Poisson shot noises. With the tractability of the asymptotic achievable rate, we formulate a new power allocation problem to maximize the weighted sum-rate of the multiuser photon-counting UOWC system. A new algorithm is developed to decompose the problem into subproblems with deterministic convexity or concavity and accordingly convexified and solved using successive convex approximation. We also propose to pre-select the subproblems, thereby reducing the complexity significantly with negligible loss of the weighted sum-rate. Simulations validate our asymptotic achievable rate, and show that the proposed algorithms can improve the weighted sum-rates of the UOWC systems by orders of magnitude, compared to the existing approaches.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".