On the application of very low rate error control coding to CDMA
Bibliographic record
Abstract
In code division multiple access (CDMA), the bandwidth of the transmitted signal is spread over a much larger bandwidth than that of the baseband signal yielding the so-called processing gain of the spread spectrum system. This spectrum spreading is usually performed using a pseudo-random (PN) sequence, but random coding analysis has indicated that bandwidth spreading using very low rate error correcting codes may lead to a larger system capacity over spreading based on PN sequence only. Hence a low rate convolutional code or combination of a low rate code and a spreading sequence could be used to improve the CDMA system capacity. We present an analysis of the CDMA capacity for the reverse, or uplink channel, from the mobile user to the base station. Using a constant overall bandwidth expansion, for a given target bit error probability we obtain the best sharing of the CDMA bandwidth between the error correcting code and the PN sequence. We can therefore evaluate the improvement in system capacity that can be obtained over more traditional CDMA systems where almost all the bandwidth expansion is due to the PN sequence spreading only.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".