Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using relays in wireless networks can potentially lead to significant capacity increases. However, within an asynchronous multi-user communication setting, relaying might cause more interference in the network, and significant sum-rate deterioration may be observed. In this work the effect of cooperation in an interference limited, narrow-band wireless network is investigated. It is crucial to determine the optimal trade-off between the amount of throughput gain obtained via cooperation and the amount of interference introduced to the network. We quantify the amount of cooperation using the notion of a cooperative region for each active node. The nodes which lie in such a region are allowed to cooperate with the source. We adopt the decode-and-forward scheme at the relays and use the physical interference model to determine the probability that a relay node correctly decodes its corresponding source. Through numerical analysis and simulation, we study the optimal cooperative region size to maximize the network sum-rate and energy efficiency, based on network size, relay availability, node decoding threshold, and destination reception capability. It is shown that optimized system performance in terms of the network sum-rate and the power efficiency is significantly improved compared with cases where relay nodes are not exploited or where the cooperative region size is suboptimal.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it