Performance analysis and power allocation for two-way multi-user mixed RF/FSO relay networks
Bibliographic record
Abstract
This paper studies the performance of two-way multiuser mixed radio frequency/free space optical (RF/FSO) relay networks with opportunistic user scheduling1and asymmetric fading channels. The considered system consists of multiple users communicating with a destination node through a decode-and forward (DF) relay in a two-way fashion. The links between the users and relay are assumed to be Rayleigh distributed RF channels, while the link between the relay and destination is Gamma-Gamma fading FSO channel. First, exact closed-form and asymptotic (high signal-to-noise ratio (SNR)) expressions are derived for the outage probability. Then, the asymptotic results are used to conduct a power optimization algorithm where exact expressions for the optimal transmission powers are provided. Additionally, performance comparisons between the considered two-way relaying (TWR) and one-way relaying (OWR) schemes under different network parameters are provided and discussed. The results show that the opportunistic user scheduling in TWR networks does not affect the network diversity order, but it enhances the system coding gain. Additionally, it is found that severe pointing error may result in a total service blockage. Additionally, the proposed power allocation scheme is found to enhance the network outage performance significantly compared to the scheme of equal power allocation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".