Interference Aware Subcarrier Assignment for Throughput Maximization in OFDMA Wireless Relay Mesh Networks
Bibliographic record
Abstract
The wireless relay mesh network (WRMN) is designed to provide robust and fault tolerant communications between relay and user nodes in broadband wireless networks. In this paper, we study interference aware resource allocation in OFDMA based WRMNs and the effects of spatial reuse on throughput. We propose an interference aware subcarrier assignment (IASA) algorithm to allocate subcarriers to links in the network such that interference is mitigated and throughput is maximized. The protocol interference model and spatial reuse are exploited to achieve the subcarrier assignment. We show that our IASA algorithm improves throughput compared to when subcarriers are used only once. However, overuse of a single subcarrier can have detrimental effects on network performance, therefore a balance must be achieved. In addition, using a maximum concurrent flow (MCF) approach, we show that under the IASA scheme of spatial reuse, throughput can be enhanced. We formulate the MCF as a linear program and solve it using dual optimization techniques. We compare our proposed algorithm with that of a graph coloring approach using a conflict graph and column generation to show that our throughput results are better than those obtained by the link coloring strategy.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".