Joint Routing and Scheduling in WiMAX-Based Mesh Networks
Bibliographic record
Abstract
The problem of scheduling and routing tree construction in WiMAX/802.16 based mesh networks is not defined in the standard and has thus been the subject to extensive research. We consider the problem of joint routing and scheduling in WiMAX-based mesh networks, with the objective of determining a minimum schedule period that satisfies a given (uplink/downlink) traffic demand. Minimizing the length of a schedule amounts to maximizing the spectrum spatial reuse by activating concurrently as many links. This group of transmission links active concurrently is referred to as the transmission group and refers to the set of wireless links that can simultaneously transmit without violating the signal-to-interference-plus-noise ratio (SINR) requirement. Our model is referred to as maximum spatial reuse (MSR). We assume centralized scheduling at the base station and attempt to maximize the system throughput through appropriate routing tree selection and achieving efficient spectrum reuse through opportunistic link scheduling. We present an ILP optimization model for the joint problem, which relies on the enumeration of all possible link schedules. Given its complexity, we decompose the problem using a column generation (CG) approach. We present two formulations for modeling MSR, namely the link-based (CGLink) and the path-based (CGPath) formulation. These two formulations differ mainly in the number of routing decision variables. Our experimental results indicate that the path-based formulation needs much less computational (CPU) time than the link-based in order to determine the (same) optimal solution with the same spatial reuse gain.
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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) | 0.001 | 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 | 0.000 |
| 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".