Particle Filtering for Mobility Enhanced Adaptive Sectoring for CDMA Uplink Capacity Maximization
Bibliographic record
Abstract
The uplink capacity of CDMA cellular networks is improved by adaptive sectoring based on tracking of mobiles' spatial distribution. The distribution is modeled as a spatial Poisson process, with its rate function quantizes the density of the active mobiles. The rate function's time dynamics is assumed to evolve according to mobiles' mobility pattern, and is formulated using the Influence model. In this paper, particle filtering is applied in the tracking and estimation of the mobile concentration based on network traffic, and it enables the computation of the network interference and thus the system outage probability. Different sectoring schemes are compared in terms of outage probabilities, and the minimum scheme is chosen for each time period. More specifically, the adaptive sectoring problem is formulated as a shortest path problem, and the optimal path corresponds to the sectoring scheme with the minimum outage probability.
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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 | 0.000 | 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 | 0.001 |
| 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 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".