Queue-Aware Joint Dynamic Interference Coordination and Heterogeneous\n QoS Provisioning in OFDMA Networks
Bibliographic record
Abstract
We propose algorithms for cloud radio access networks that not only provide\nheterogeneous quality of-service (QoS) for rate- and, importantly,\ndelay-sensitive applications, but also jointly optimize the frequency reuse\npattern. Importantly, unlike related works, we account for random arrivals,\nthrough queue awareness and, unlike majority of works focusing on a single\nframe only, we consider QoS measures averaged over multiple frames involving a\nset of closed loop controls. We model this problem as multi-cell optimization\nto maximize a sum utility subject to the QoS constraints, expressed as minimum\nmean-rate or maximum mean-delay. Since we consider dynamic interference\ncoordination jointly with dynamic user association, the problem is not convex,\neven after integer relaxation. We translate the problem into an optimization of\nframe rates, amenable to a decomposition into intertwined primal and dual\nproblems. The solution to this optimization problem provides joint decisions on\nscheduling, dynamic interference coordination, and, importantly, unlike most\nworks in this area, on dynamic user association. Additionally, we propose a\nnovel method to manage infeasible loads. Extensive simulations confirm that the\ndesign responds to instantaneous loads, heterogeneous user and AP locations,\nchannel conditions, and QoS constraints while, if required, keeping outage low\nwhen dealing with infeasible loads. Comparisons to the baseline proportional\nfair scheme illustrate the gains achieved.\n
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".