Network utility 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. While heterogeneous networks (HetNets) are expected to offer diverse radio access capabilities and improve the transmission performance of user equipments (UEs) significantly through integrating various RANs efficiently, the backhaul links may still experience challenges in offering quality of service (QoS) guaranteed services to UEs. To tackle 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 HetNets. Stressing the tradeoff between user download delay and caching cost, we introduce the concept of 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 Lagrange partial relaxation and McCormick envelopes, and then propose an iterative algorithm. Within each iteration, for a given set of Lagrange multipliers, the three subproblems are solved respectively by means of the modified Kuhn-Munkres (K-M) algorithm and the locally optimal solutions can be obtained, based on which the Lagrange multipliers can be updated through applying subgradient method. Simulation results demonstrate the effectiveness of the proposed algorithm.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".