Adaptive cell sectoring using fixed overlapping sectors in CDMA networks
Bibliographic record
Abstract
The problem of base station antenna assignment (BSAA) with minimum mobile transmit power (MTP) is studied for CDMA networks that employ fixed overlapping sector antenna architecture (FOSAA). It is noted that the non-FOSAA has limitations in switching users between in-cell sectors and also out-of-cell sectors in moderately loaded networks. It is then shown that by employing overlapping sectors in FOSAA, we can exploit the flexibility of assigning a user to one of possibly many potential antenna to effectively support the non-uniform angular traffic. It is also proven that the problem of selecting a set of antenna from a pool of overlapping antenna and assigning the users to them in FOSAA with minimum MTP is a special case of a general problem that was solved by Hanly (1995) and Yates (1995). The process of dynamic cell sectoring is differentiated two-fold as cell-breathing (CB) and cell-slicing (CS) and the latter can be viewed as azimuthal counterpart of the former radial scheme. The hybrid scheme, CB+CS, is shown to yield the optimal solution in minimum total MTP in a CDMA/FOSAA system. The performance results for the total MTP and the received signal quality are reported. As the congestion level increases, the difference in SIR performance between CB and CS schemes becomes more apparent with the latter outperforming the former. The performance results also show that on average, the CB scheme requires about 30% more power than in CB+CS, when 60% of the mobiles are concentrated in a hot-spot sector in a conventional 3-sector cell.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".