Spectrum efficiency optimization in a cellular packet data communication system
Bibliographic record
Abstract
A simulation model of multiple co-channel interferers in a cellular communication system, for downlink traffic using non-coherent frequency shift keying modulation, is developed and used to investigate the bit error rate (BER) and block error rate (BKER) of the packet data traffic as a function of the number, n/sub I/, of interferers. It is found that for large n/sub I/ the interference signal can be modeled as white Gaussian noise of the same power spectral density (PSD) value. The results are used to determine the spectrum efficiency, E/sub s/, for different system configurations. The optimal value, K/sub opt/ of the cluster size is studied. With no fading or very slow Rayleigh fading, K/sub opt/ is small, typically less than 4. However, K/sub opt/ increases with the Doppler frequency. The use of FEC tends to lower K/sub opt/ but does not necessarily improve E/sub s/.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".