QoS-driven MAC-layer resource allocation for wireless mesh networks with non-altruistic node cooperation and service differentiation
Bibliographic record
Abstract
Node cooperation has been demonstrated promising in system performance improvement for wireless networks. To effectively provision packet-level quality-of-service (QoS) in wireless mesh networks (WMNs) supporting heterogeneous traffic, medium access control (MAC) with service differentiation is imperative. In this paper, we study the problem of non-altruistic non-reciprocal node cooperative resource allocation for WMNs with QoS support, taking subcarrier allocation, power allocation, partner selection/allocation, service differentiation, and packet scheduling into account. Due to the NP hardness of our resource allocation problem, we propose two low-complexity yet effective approaches based on the Karush-Kuhn-Tucker (KKT) interpretations, tailored for WMNs with QoS assurance and MAC-layer service differentiation. Further, simulation results show that both proposed approaches can effectively provision packet-level QoS and enhance system performance. Our study also sheds some light on the question of whether and when non-altruistic node cooperation is beneficial to WMNs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".