Performance Analysis of Multiple Association in Ultra-Dense Networks
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Bibliographic record
Abstract
In this paper, we propose a general mathematical framework to compute the average downlink rate in a multiple connectivity context considering ultra-dense network (UDN) environment. UDN is a dense small cells network featured by the high density of small cells that may exceed the density of active users. In multiple association, a user connects to M base stations (BSs) that provide the maximum average received power forming a multicell. This provides the user with a “data-shower,” where the user's traffic is split into multiple paths, which helps overcoming the capacity limitations imposed by the backhaul links. The developed framework significantly simplifies the computation of the average downlink rate of the individual connections to the cells of a multicell. Moreover, the accuracy of the mathematical framework is confirmed by extensive simulations. The simulation results show a perfect match with the numerical results computed from the mathematical framework in different combinations of the system parameters including multicell size, small cells density, active users density, pathloss exponent, and fading channel distribution of the signal link.
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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 | 0.001 | 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