Provisioning backhaul traffic flows in TDMA/OFDMA infrastructure Wireless Mesh Networks with near-perfect QoS
Bibliographic record
Abstract
Scheduling and channel assignment algorithms to provision longer-term backhaul traffic flows in infrastructure TDMA/OFDMA Wireless Mesh Networks (WMNs) with near-perfect QoS are described. A recursive fair stochastic matrix decomposition algorithm is used to compute transmission schedules for all provisioned backhaul traffic flows between BSs. Each schedule consists of a sequence of permutations which specify active edges, which provide near-minimal delay and jitter and near-perfect QoS guarantees on a per-flow basis. A constrained graph coloring algorithm is used to color the permutations, to remove primary conflicts and minimize secondary conflicts. The colored edges are assigned to time-slots and are used to compute the antenna beamforming vectors and transmission power levels. The beamforming can use either a zero-forcing or an iterative MMSE algorithm. Each wireless link achieves a prescribed transmission rate and SINR such that the total transmission power in the WMN is minimized, subject to the per-flow QoS constraints. Extensive simulations of an essentially-saturated hexagonal TDMA/OFDMA WMN supporting backhaul traffic flows are reported.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".