Artificial Intelligence Assisted Softwarization, Virtualization and Intelligentization of Everything for Improving Network Access
Bibliographic record
Abstract
Future warfare will require commanding and controlling resources across multiple warfighting domains (i.e., sea, land, air, space, and cyber). Successfully performing command and control relies on reliable and accessible information aggregated from varied sensors. Connecting the sensors in contested environments needs a reliable communications network. This paper investigates the potential of AI-assisted softwarization, virtualization, and intelligentization of everything to improve network access in contested environments significantly. Using multiclass queueing theory, we optimize the allocation of resources to multiple services in a network-slicing environment. Our approach considers the limitations imposed by the dynamic spectrum allocation and the need for reliability and low latency in military communications. Some of the limitations imposed by the dynamic spectrum allocation in military communications include spectrum jamming and radio-triggered roadside bombs. We demonstrate that AI-assisted softwarization, virtualization and intelligentization of everything can significantly improve network access by increasing the carried traffic load at base stations. The carried traffic load is increased due to the enhanced number of physical links available at those base stations. The analysis results indicate that the carried traffic load or the number of physical links can be increased by 100,000. As our model and analysis are limited to the base stations and do not consider the network as a whole, future work will address these limitations by modelling the entire network and exploring the impact of jamming.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".