Performance of space-time spreading in DS-CDMA systems over fast-fading channels
Bibliographic record
Abstract
In this paper we investigate the multiuser performance of a space-time spreading scheme that uses space-time block codes (STBCs) to enhance the received signal quality by utilizing the spatial and temporal diversities at the receiver side. We study the performance of the transmit diversity scheme introduced in the literature for a DS-CDMA system using orthogonal and nonorthogonal spreading codes over Rayleigh fast-fading channel with two antennas at the transmitter and one in the receiver side. For the single user case, and using orthogonal spreading codes, we develop the performance analysis for the underlying transmit diversity scheme over fast-fading channels. We prove that using such a space-time spreading scheme can achieve a twofold of the diversity order obtained using existing space-time spreading schemes. To examine the effect of signal interference on the receiver performance, we consider the case of nonorthogonal codes where we employ a linear minimum-mean square-error (MMSE) multiuser detector as a suboptimum linear detector. The performance of the multiuser system is examined for a moderate number of users where we show that the diversity order is still maintained and only a SNR loss is incurred due to the residual interference. Finally, we compare the performance of the MMSE multiuser detector to a simple adaptive MMSE combining scheme.
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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 | 0.008 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".