Performance of multidimensional multicode DS-CDMA using code diversity and error detection
Bibliographic record
Abstract
High rate transmission can be realized using multiple orthogonal codes (MOC), as proposed in the third-generation wide-band code-division multiple-access (W-CDMA) standard. However, the linear sum of MOC channels is no longer constant amplitude, and a highly linear, power-inefficient amplifier may be required for transmission. Recently, a nonlinear block coding technique called precoding is introduced to maintain a constant amplitude signal after superposition of MOC channels. This is achieved by adding redundancy. In this paper, we first describe a multidimensional signaling scheme that recovers some information rate loss by precoding. Second, we propose a self-interference (SI) cancellation scheme resulting from a code diversity between the in-phase and quadrature subchannels among MOC channels. In a typical wireless channel with multipath fading, this type of SI can be detrimental especially when the number of parallel MOC channels is large. Third, we show that the error detection capability of precoding can be combined with code diversity, resulting in a diversity gain. In addition, we show that the diversity gain can be achieved using antenna diversity to assure the degree of freedom in code diversity, and even with the large number of MOC channels, the error performance can be maintained reliably while outperforming the variable spreading factor scheme in W-CDMA.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".