Coding-spreading tradeoff analysis for DS-CDMA systems
Bibliographic record
Abstract
The best tradeoff between coding and spreading in a single-cell direct-sequence code division multiple access (DS-CDMA) system is investigated. The best code rate in terms of the system spectral efficiency for a single-class system and the optimal power allocation for a multi-class system is analyzed by applying both a matched filter (MF) receiver and a minimum mean square error (MMSE) receiver. It is shown that for the MF receiver, the coding-spreading tradeoff favors a code rate reduction. In the case of the MMSE receiver, the spectral efficiency vs. code rate curve is convex, so there is a best code rate corresponding to a given E/sub b//N/sub 0/ specification. Numerical results show that the best code rate is a function of the system load, the required bit error rate, and the steepness of the required SIR vs. the code rate curve, i.e., the error correction capability of the applied coding codes. The best code rate to maximize the spectral efficiency is further related to the system design parameter E/sub b//N/sub 0/.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".