Efficiently computable bounds on the rates achieved by a cross layer design with binary scheduling in generic OFDMA wireless networks
Bibliographic record
Abstract
Future broadband communication networks are expected to be OFDMA-based with generic ad hoc topologies, wherein the wireless nodes play multiple roles, including transmission, reception and relaying. When the schedules by which the OFDMA subchannels are assigned to wireless links are binary, obtaining a characterization of the maximum rates that can be reliably communicated by these networks can be shown to be NP-hard. To circumvent this difficulty, we provide efficient means for computing two lower bounds on the achievable weighted sum rates. The first bound is obtained by using geometric programming approximation techniques, whereas the second bound is obtained by rounding the solutions of a relaxed version of the cross layer design problem. Finally, we consider an existing upper bound on the achievable weighted sum rates, and we use numerical simulations to show that the lower and upper bounds are relatively tight, especially at high signal-to-noise ratios.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".