On robust allocation policies in wireless heterogeneous networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In fourth generation (4G) wireless networks, users are able to roam freely from one type of wireless access network to another while preserving the main characteristics of their connections. While various aspects of this vision have been discussed since mid 1990s, there remain fundamental challenges. Of these challenges is performing resource management across different access networks while considering various irregularities. This work advocates that any proposal for resource management in such networks should act reactively toward operational dynamicity while proactively allocating resources in a manner that sustains demand uncertainty. It shows how proactive allocations can be made using a formulation based on stochastic programming. The objective is to maximize the allocations while minimizing underutilization and rejection. It also discusses how a comprehensive proactive module can be realized. To the best of our knowledge, this is the first attempt to directly address joint resource management in wireless heterogeneous networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it