On the impact of correlated shadowing on the performance of user-in-the-loop for mobility
Bibliographic record
Abstract
The cellular network users are demanding more traffic and expecting a ubiquitous high data rate. Several techniques are investigated to cope with this massive demand. All the current solutions are focusing on improving this issue from the supply side. However it is expected that a significant improvement on the system performance can be gained if the end users become a part of the system and not just consumers. The user-in-the-loop (UIL) spatial approach provides a solution to fulfil the increasing traffic demand by convincing users to move to locations with higher SINR values. The previous results of the UIL are promising and a higher cell spectral efficiency is achieved only by the involvement of the users and without any investment on the supply side. UIL is a user-centric approach that offers an incentive to influence users' behavior to participate in the system improvement process. In this paper, the previous ideal environment of UIL is extended by integrating the important effect of correlated shadowing to the analysis of UIL performance. The obtained results in this paper confirm the previous results and promise significant performance improvements. Results show that the achieved higher spectral efficiency and the required moving distance depend on the correlation parameters.
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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 | 0.001 | 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 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".