Economically Optimal MS Association for Multimedia Content Delivery in Cache-Enabled Heterogeneous Cloud Radio Access Networks
Bibliographic record
Abstract
In cache-enabled heterogeneous cloud radio access networks (HC-RANs), mobile station (MS) association for multimedia content delivery should consider both the content caching location and the wireless channel quality. This paper studies economically optimal MS association to tradeoff the cache-hit ratio and the ratio of MSs with satisfied quality of service (QoS). When the associated enhanced remote radio unit (eRRU) stores the requesting content, the content can be fetched directly from the local cache. Otherwise, fronthaul has to be used to fetch the content. The use of fronthaul resource and cache is treated as costs, and payments of QoS-satisfied MSs are treated as incomes. Thus, the economic MS association is formulated as an optimization problem to maximize the system utility, i.e., total profit of the network operator, which is defined as the difference between incomes and costs. A belief propagation-based method is employed to solve the problem on a developed factor graph. Simulation results show that the proposed economically optimal MS association achieves much higher profit than the existing schemes and works well in the network with various loads. Moreover, the profit of the proposed scheme can be improved with inter-cell interference coordination. For the case with extremely skewed content popularity, the proposed scheme can avoid MS overloading at eRRUs storing most popular multimedia contents. Furthermore, it can support more MSs with satisfied QoS, which leads to a higher profit.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".