Throughput Analysis of Multiple Access Relay Channel under Collision Model
Bibliographic record
Abstract
Despite much research on the throughput of relaying networks under idealized interference models, many practical wireless networks rely on physical-layer protocols that preclude the concurrent reception of multiple transmissions. In this work, we develop analytical frameworks for the uplink of a multi- source single-channel relay-aided wireless system where transmissions are scheduled to avoid collisions. We study amplify-and-forward and decode-and-forward strategies, in both time-sharing and network-coded variants, and provide mathematical models to investigate their achievable rate regions. Both general and optimal power allocations are considered. We also find the cut-set outer bounds for the rate regions. Moreover, we present a comparison between these methods with the simple time sharing scheme. Our numerical results reveal that optimizing power allocation favors the time sharing scheme significantly more than it does the relaying schemes, so that time sharing under some circumstances can provide higher maximum sum rates, even if the links to the relay have strong channel gains. The proposed analysis provides a means to quantitatively evaluate the efficacy of relaying under the collision model, leading to pragmatic design guidelines.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".