A transmit diversity scheme using space-time spreading for DS-CDMA systems in Rayleigh fading channels
Bibliographic record
Abstract
The application of space-time block coding (STBC) in the downlink of a communication system has shown to improve the quality of the received signal at the mobile user by placing the spatial diversity at the base station. In this paper, we propose a simple space-time spreading scheme for code-division multiple-access (CDMA) systems over fast-fading channels. The proposed transmit diversity scheme is based on two transmit and one receive antenna and is suitable for Rayleigh fast-fading channels. In this we employ orthogonal spreading codes to exploit the time diversity introduced by the channel, and hence a two-fold of the diversity order obtained using existing space- time spreading schemes is achieved. Nevertheless, for slowly- fading channels, we show that the proposed coding scheme reduces to existing schemes introduced in the literature with no performance degradation. Finally, we examine the effect of using nonorthogonal spreading codes on the receiver performance. Our results show that using a simple adaptive decoder, based on the minimum mean-squared error (MMSE) criterion, the diversity order is still maintained and only a small loss in the signal-to- noise ratio is incurred relative to the ideal case with orthogonal codes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".