Capacity Planning for Group-Mobility Users in OFDMA Wireless Networks
Bibliographic record
Abstract
Because of the random nature of user mobility, the channel gain of each user in a cellular network changes over time causing the signal-to-interference ratio (SNR) of the user to fluctuate continuously. Ongoing connections may experience outage events during periods of low SNR. As the outage ratio depends on the SNR statistics and the number of connections admitted in the system, admission capacity planning needs to take into account the SNR fluctuations. In this paper, we propose new methods for admission capacity planning in orthogonal frequency-division multiple-access (OFMDA) cellular networks which consider the randomness of the channel gain in formulating the outage ratio and the excess capacity ratio. Admission capacity planning is solved by three optimization problems that maximize the reduction of the outage ratio, the excess capacity ratio, and the convex combination of them. The simplicity of the problem formulations facilitates their solutions in real time. The proposed planning method provides an attractive means for dimensioning OFDMA cellular networks in which a large fraction of users experience group-mobility.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".