Concatenated orthogonal/PN spreading scheme for cellular DS-CDMA systems with integrated traffic
Bibliographic record
Abstract
The application of the concatenated orthogonal/PN spreading scheme for integrated traffic is introduced. Bhargava (1994) proposed using a single line rate (adjusted data rate before spreading) to accommodate traffic with a wide range of source rates. For traffic with source rates higher than the line rate, the authors propose using concatenated orthogonal/PN spreading sequences to subdivide a high rate stream into several parallel line rate streams. The performance of the concatenated orthogonal-PN spreading sequence for homogenous voice traffic in various cellular mobile environments with multipath fading, log-normal shadowing and path loss, is first analyzed and compared with that of the conventional non-concatenated long PN sequence. The authors then evaluate the performance of a system with integrated traffic of voice and video. In conjunction with this, they propose the use of cosets of Walsh-Hadamard (WH) codes to reduce the multi-user interference. Different methods of assigning the spreading sequences among the cosets which improve the capacity of both voice and video users, are investigated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".