Wireless Network Virtualization in Uplink Coordinated Multi-Cell MIMO Systems
Bibliographic record
Abstract
We consider wireless network virtualization (WNV) in the uplink of a coordinated multi-cell system, where multiple service providers (SPs) operate in virtually isolated networks managed by an infrastructure provider (InP). The InP provides service isolation among the SPs by exploiting the spatial structure in MIMO communication. We jointly optimize the uplink receive beamforming at the base stations (BSs) and the transmit power of the SPs' subscribing users, by alternating between two subproblems that both admit efficient closed-form solutions. We then propose a distributed implementation that solves each subprobem among the BSs without the need for a central controller. We show that the distributed approach requires significantly less communication overhead compared with the centralized one, especially when the system is not overloaded. Our simulation results under typical wireless networking environments demonstrate that the proposed solution enables effective network virtualization, to support the independent operation of multiple SPs over multiple cells, without losing communication efficiency compared with non-virtualized network operation. Furthermore, it substantially outperforms traditional WNV based on strict resource separation, especially for systems with a large number of antennas or a large number of SPs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".