Frequency selection strategies for hybrid TDD/FDD-CDMA cellular networks
Bibliographic record
Abstract
Hybrid TDD/FDD-CDMA is an attractive technique for third generation cellular networks, in which FDD-CDMA is used to provide wide area voice and low bit-rate data services, and TDD-CDMA with limited coverage is used to provide high bit-rate asymmetric data services. In this paper, we propose two new frequency selection strategies to select the FDD-CDMA channel (forward link f/sub F/ or reverse link f/sub R/) for TDD-CDMA picocell operation in a FDD-CDMA macrocellular network. One is random selection strategy (RSS) which chooses f/sub F/ or f/sub R/ in a random fashion, and the other one is distance based selection strategy (DBSS) which applies a cell partitioning structure to divide each macrocell into an inner tier and an outer tier. With two separated carrier frequencies (f/sub F/ and f/sub R/), the inner tier of each macrocell can use one of these two frequencies, while the outer tier can use the other one. The outage performance of the hybrid TDD/FDD-CDMA network employing the above strategies is studied through computer simulations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".