An efficient cross layer design for OFDMA-based wireless networks with channel reuse
Bibliographic record
Abstract
This paper considers a joint design that incorporates the physical, medium access and network layers of a generic OFDMA-based wireless network with an ad hoc topology. The network employs channel reuse, whereby a frequency subchannel might be used simultaneously by multiple nodes. In addition to being a source and/or a destination, each node can act as a half-duplex relay to assist other nodes. The design objective is to determine the jointly optimal data routes and subchannel power allocations that maximize a weighted sum of the rates that can be reliably communicated over the network. Assuming that the signals transmitted by the nodes are Gaussian, the joint cross layer design of routing and power allocation is cast as an optimization problem. Unfortunately, this problem is non-convex, and hence difficult to solve. To circumvent this difficulty, an efficient technique based on geometric programming is developed to obtain a local solution that satisfies the Karush-Kuhn-Tucker necessary optimality conditions. Numerical results show that, despite the potential suboptimality of the obtained solution, for some network scenarios, it offers significant gains over optimal scheduling-based schemes in which a frequency band is allowed to be used by one node only at any time instant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 0.001 |
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".