Latency Driven Fronthaul Bandwidth Allocation and Cooperative Beamforming for Cache-enabled Cloud-based Small Cell Networks
Why this work is in the frame
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Bibliographic record
Abstract
This paper considers content delivery of the cache-enabled small cell networks (C-SCNs), where users with the same request form a multicast group and are served by a cluster of small-cell base stations (SBSs) under the coordination of the central processor. The performance of such a coordination is severely limited by the fronthaul link, which may be saturated and degrade quality of service (QoS). To improve user QoS, we propose a latency driven scheme by jointly optimizing fronthaul bandwidth allocation, multicast beamforming, and BS clustering. Accordingly, with min-max fairness among multicast groups, a latency minimization problem is formulated under the constraints of fronthaul bandwidth and transmission power. The resultant problem is a mixed-integer nonlinear program, which is NP-hard. To address such a complex problem, a quadratic penalty-based algorithm is proposed by using a reformulation of binary constraint. Meanwhile, we present the necessary condition for an optimal solution, which shows that fronthaul bandwidth allocation is inherently adaptive to cached contents and patterns of BS cooperation. Finally, simulation results demonstrate that the proposed scheme can effectively reduce latency under different caching strategies.
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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