An efficient predictive admission control policy for heterogenous wireless bandwidth allocation in next generation mobile 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
Next generation mobile networks (NGMN) are expected to integrate several heterogenous wireless technologies in order to provide high system capacity and cost effective global service coverage. In this paper we propose an efficient predictive admission control policy for heterogenous wireless bandwidth allocation. We predict well chosen traffic parameters using neural networks and we estimate blocking probabilities using generally distributed traffic models. Furthermore, we use a Tabu search algorithm to find the optimal guard band for a multi-layer heterogenous NGMN. The objective of our multi-layer predictive admission control policy (MLPAC) is to minimize global blocking probability while guaranteeing a hard constraint on handoff dropping probability. It extends the overflow scheme used in two-layer hierarchical cellular systems (HCS) to multiple heterogenous access technologies in NGMN. Presented results show that our MLPAC approach is more efficient in allocating the scarce heterogenous wireless bandwidth to a higher number of accepted connections while maintaining minimal guard bands for horizontal and vertical handoff protection.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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