Bibliographic record
Abstract
In this paper, we consider the capacity of cellular code division multiple access (CDMA) when there is out-of-band ad hoc traffic relaying. The mobile stations (MSs) are dual-mode, having both ad hoc and cellular CDMA radios. An active MS is free to choose any available relay station (RS) within its ad hoc radio coverage area for dual-hop communication with the CDMA base station (BS). Communications between the RSs and the MSs use bandwidth which is available to the ad hoc radio and does not consume the CDMA capacity. Using this mechanism, CDIVIA interference can be reduced by dynamically selecting RSs which have more favorable CDMA link characteristics. Several relay station selection criteria are considered, namely, ad hoc relaying with low relative interference (ARRI), with best link gain (ARLG), and with shortest distance (ARSD). The relay station selection protocols are compatible with existing wireless local area network (WLAN) standards such as IEEE 802.11. An analytic model is used to compute the effects on uplink and downlink CDMA capacities when out-of-band relaying is added. The results show that very significant capacity improvements are possible by using these criteria compared with conventional CDMA with hard or soft handoff. Ad hoc relaying which dynamically tracks CDMA link quality can achieve greater capacity improvements than that using a distance-based relay station selection. Relaying, which considers both signal and interference conditions, achieves better capacity than that based on signal link quality alone.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".