Joint routing, scheduling and power allocation in OFDMA wireless ad hoc networks
Bibliographic record
Abstract
In this paper an OFDMA-based wireless ad hoc network is considered. In addition to the potential of being a source and/or a destination, each node is assumed to be capable of decoding and forwarding its received packets to other nodes in the network. The goal is to determine the optimal data routes, subchannel schedules, and power allocations that maximize a weighted sum rate of the data communicated over the network. Two instances of this problem are considered. In the first instance, each subchannel is exclusively used on one of the links, whereas in the second instance subchannels are allowed to be time shared by multiple links. The first problem gives rise to an NP-hard mixed integer optimization problem that is difficult to solve. In contrast, using a change of variables, the second problem is cast in a convex form, which is amenable to highly efficient interior point solvers. Simulation results suggest that the gain in the weighted sum rate achieved by the relaxation in the second problem over that achieved by the original mixed integer problem is negligible for small networks, and increases with the size of the network.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".