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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".