Performance Evaluation of Relay Deployment Strategies in Multi-Cell Single Frequency Networks
Bibliographic record
Abstract
We investigate the impact of fixed relay station deployment in a single frequency network (SFN) using orthogonal frequency-division multiplexing (OFDM). We provide semi-analytical methods for relay-based SFN performance evaluation. Monte Carlo simulations are performed to provide numerical calculation of multi-cell network performance. The performance metric is the cumulative distribution function of the signal-to-interference-plus-noise-ratio (SINR) for different user positions in the network. Common relaying methods for two-hop amplify-and-forward (A&F) relay networks are evaluated. The impact on the SINR is compared for different relaying gains, locations and densities of relays, as well as different transmission protocols, such as full-duplex (FD) and half-duplex (HD). Overall, taking into account their different rates and physical layer performances, FD appears to outperform HD. In addition to showing the benefit of relay deployment for enhancing network performance, our simulations show that the fixed and variable gains perform equally well.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".