Decoupled optimization of interference aware routing and scheduling for throughput maximization in 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 investigate the benefits of decoupled optimization of routing and scheduling in WRMNs using the physical interference model and spatial reuse to maximize overall throughput. We model the routing optimization as a linear program using multicommodity flows (MCF). We refer to this problem as multicommodity flow routing optimization (MCF-ROPT). Using the flow per link determined from MCF-ROPT, we develop an optimization formulation to schedule the link traffic such that interference is minimized and time slots are reused appropriately based on spatial TDMA (STDMA). Furthermore, our scheduling approach incorporates the effect of reuse of multiple carriers on the transmission schedule. We refer to this problem as SM-TSS (STDMA multicarrier traffic sensitive scheduling). The SM-TSS is NP-hard and thus is solved using column generation. We compare our formulations with decoupled optimizations that use the protocol interference model and/or single carrier systems and show that our approach guarantees higher throughput by mitigating interference effectively.
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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) | 0.001 | 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) | 0.001 | 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".