Resource Provisioning in Wireless Virtualized Networks via Massive-MIMO
Why this work is in the frame
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Bibliographic record
Abstract
This letter proposes a dynamic resource provisioning scheme for an OFDMA wireless virtualized network (WVN), where one base-station equipped with a large number of antennas serves users belonging to a number of service providers via different slices. In particular, joint power, sub-carrier, and antenna allocation problems are presented for both perfect and imperfect channel knowledge cases, aiming to maximize a sum-utility while maintaining a minimum rate per slice. Subsequently, relaxation and variable transformation are applied to develop the efficient algorithm to solve the formulated non-convex, combinational optimization problem. Simulation results reveal the benefits of applying a large number of antennas in this setup and evaluate the network performance for different system conditions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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