An analysis of the CDMA capacity using a combination of low rate convolutional codes and PN sequence
Bibliographic record
Abstract
In wireless personal communication services systems, all end users access the communication channel by sharing a common frequency bandwidth. Therefore efficient multiple access techniques must be implemented in order to allow all users in a given area to access the communications network facilities. Of particular interest is the code division multiple access scheme which allows random asynchronous multiple access by the users while offering a capacity-performance trade off, that is, accepting a larger number of users with a lower error performance. Therefore one of the most important factor when evaluating CDMA or any other multiple access technique is the system capacity. The shortage of optimal very low rate convolutional codes in the literature has led to a class of new quasi-optimal very low rate codes, called nested convolutional codes. These codes which are easy to construct provide a free distance that is very close to the Heller bound, therefore producing a substantial coding gain. Especially important for CDMA, these coding gains can be translated into system capacity improvements. 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, we obtain the best sharing of the CDMA bandwidth between the error correcting code and the PN sequence and show 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.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".