Novel Distributed Scheduling Algorithms for mmWave Mesh Networks
Bibliographic record
Abstract
This paper addresses throughput improvement in millimeter-wave (mmWave) mesh networks via two novel distributed scheduling algorithms. The first one uses packet aggregation and block acknowledgment (ACK) that were introduced in the IEEE Std 802.11e-2005 for WiFi. Specifically, a distributed time-division multiplexing scheduling algorithm, which targets increasing the network capacity via reserving as many contiguous slots as possible for each node, is proposed thus enabling packet aggregation. This algorithm achieves its goal when the operating signal-to-noise ratio (SNR) is significantly high. If that is not the case, the second proposed algorithm can be used. It is a distributed one that starts initially with a random feasible schedule determined cooperatively between nodes. The algorithm then tries to reach better feasible schedules via parallel and successive local searches without violating feasibility constraints. Extensive simulations show that the first algorithm improves the network throughput by almost [Formula: see text] compared to the well-known memory-guided directional medium access control (MDMAC) due to reducing the transmission overhead. The second proposed algorithm is shown to increase the number of reserved slots by about [Formula: see text] over MDMAC. Both algorithms are shown to either increase or almost maintain the same degree of fairness among the nodes as quantified by Jain’s fairness index.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.000 |
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".