Cache-Aware Multicast Beamforming Design for Multicell Multigroup Multicast
Bibliographic record
Abstract
To promote the massive video content delivery and to realize the long-term overall cost, the caching and computing functions have to be installed at some intermediate nodes within the networks. This paper presents a cache-aware multicast beamforming design for multicell multigroup multicast, where information-centric networking and mobile edge computing techniques are brought in the multicell multicast system to cache and transcode the contents passing through the nodes. The proposed cache-aware multicast beamforming design jointly optimizes the multicast mode, the caching strategy, and the network-wide beamforming vector, and focuses on minimizing the energy cost of the caching, computing, and communications. To make the formulated problem tractable, a two step method is proposed in this paper, where the first step is devoted to the cache-aware multicast approach design, while the second step is focused on the sparse multicast beamforming design. Furthermore, in order to promote the stabilization of the system, we further design a robust joint optimization strategy for the scenario with the imperfect channel state information. Extensive simulations are conducted to evaluate the performance of our proposed schemes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".