Joint user association and resource allocation in small cell networks with backhaul constraints
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Heterogeneous networks potentially provide significant capacity gains by overlaying the traditional cellular network with a layer of small cells (SCs) served by access points (APs). However, the limited backhaul capacity of the SC APs, combined with increased interference from neighboring cells, necessitates careful resource allocation to realize the gains. In our work, we consider maximization of the weighted sum rate in small cell networks with carrier aggregation while enforcing a backhaul constraint on each SC AP. We propose an efficient, waterfilling-like, algorithm which converges to a locally optimal solution of the non-convex optimization problem. This algorithm differs from existing works by using a computationally efficient bisection-like search that ensures the sum-power and sum-rate constraints at each AP are satisfied via an one-dimensional search. An added advantage is that this approach also allows for a decentralized implementation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it