Channel Time Allocations and Handoff Management for Fair Throughput in Wireless Mesh Networks
Bibliographic record
Abstract
In this paper, we study a wireless mesh network (WMN), where a number of access points (APs) form a wireless infrastructure and provide communications to the mobile stations (MSs). Different APs share the same frequency channel. We study how to provide fair long-term throughput for the MSs while efficiently utilizing the channel resources through effective handoff management and channel timeline allocations, where the channel time is allocated at two levels: first among the APs and then among the MSs. An optimization problem is first formulated and solved. The optimum solution is based on the assumption of having global information about the channel conditions of all the MSs and cannot be easily implemented in a practical WMN. Two distributed schemes are proposed by decoupling the handoff management and channel time allocations. The HO-CA scheme performs heuristic handoff decisions for the MSs based on their link gains to nearby APs and then optimizes the channel time allocations through an iterative process. The CA-HO scheme allocates the channel time to individual APs based on interfering relationship of the APs and then allows the MSs to make handoff decisions based on possible utilities from nearby APs. In both schemes, individual APs solve a local optimization problem to allocate channel time for their associated MSs. Numerical results indicate that both the proposed schemes can achieve very good fairness and that the HO-CA scheme achieves higher throughput.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".