Power Minimization in Wireless Network Virtualization with Massive MIMO
Bibliographic record
Abstract
This paper presents a new method of wireless network virtualization to share the downlink of a massive-MIMO base station among multiple Service Providers (SPs). The SPs are allowed to simultaneously utilize all antennas and channel resource of the Infrastructure Provider (InP). This can improve resource utilization but also requires the InP to control the interference between SPs that are oblivious of each other. We develop novel precoding schemes to minimize the InP's transmission power while satisfying certain prescribed maximum deviation between each SP's intended signal to its users and what the InP delivers. This problem is studied for both perfect and imperfect channel state information (CSI). Under perfect CSI, the proposed precoding is optimal and substantially outperforms a time-sharing alternative. Under imperfect CSI, we use a numerical lower bound to show that the proposed precoding is nearly optimal.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".