Cache-Aware Multicast Beamforming Design for Multicell Multigroup Multicast
Why this work is in the frame
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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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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 | 0.001 | 0.001 |
| 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 it