Improving throughput by fine-grained channel allocation in cooperative wireless networks
Bibliographic record
Abstract
Cooperative communication provides an efficient and low-cost way to achieve spatial diversity without deploying multiple antennas on each node in wireless networks. In a channel-constrained environment, such as cognitive radio networks, the channel allocation as well as relay assignment have been identified as two critical factors in determining the performance of multiple source-destination pairs. However, the advantage of channel diversity has little been exploited in such networks under a simplified model where the transmissions within a cooperative communication pair are on a common channel. In this paper, we consider a fine-grained channel allocation scheme that the source and the relay can work on different channels to complete a signal transmission. We study its performance gain in maximizing the minimum throughput among multiple source-destination pairs in channel-constrained wireless networks with a number of dedicated relay nodes. This problem is proved to be NP-hard and an online algorithm is proposed for a dynamic wireless network where the accessible channels of each node may vary from time to time. Extensive simulations are conducted to show that the proposed fine-grained channel allocation scheme can effectively improve the performance under various network settings.
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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 | 0.005 |
| 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 | 0.001 |
| 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".