Utility function optimization based joint user association and content placement in heterogeneous networks
Bibliographic record
Abstract
The rapid growth of traffic demands has posed challenges and difficulties on both the radio access networks (RANs) and the backhaul links. To stress these problems, caching technology, more specifically, caching user contents at the infrastructures of different RANs is proposed as an effective approach. In this paper, we consider the joint user association and cache content placement problem in cache-enabled heterogeneous networks (HetNets). Stressing the tradeoff between user download delay and caching cost, we define a utility function which characterizes the joint network performance as the weighted sum of user download delay and the caching cost and formulate the joint user association and cache content placement problem as a network utility optimization problem. As the formulated optimization problem is a nonlinear integer optimization problem which cannot be solved conveniently using traditional optimization tools, we transform the original optimization problem equivalently into three convex subproblems by applying McCormick envelopes and Lagrange partial relaxation, and then solve the subproblems by means of Kuhn-Munkres (K-M) algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".