Space-time coded uplink transmission with decision feedback sequence estimation
Bibliographic record
Abstract
In this paper, we address the problem of adopting space-time (ST) coding in the uplink of DS-CDMA systems. A novel system architecture, which is the combination of single-carrier time-reversal zero-padded (SC-TR-ZP) based ST block coding with chip-interleaved block-spread (CIBS)-CDMA, is proposed for frequency selective uplink transmissions. Simulation results show that a substantial performance improvement can be achieved by adopting ST coding compared to the original CIBC-CDMA scheme without ST-coding. Optimal maximum likelihood sequence estimation (MLSE) may be computationally prohibitive for long channels and/or with high-level modulation. Hence, the performance of different decision feedback sequence estimation (DFSE) schemes are investigated for the proposed ST block coded uplink system. In the case of whitening DFSE, a linear prediction (LP)-based approach is adapted for designing a whitening prefilter. The proposed combined linear equalization-modified unwhitened DFSE (Comb. LE-MUDFSE) scheme seems to be very attractive as error floor behavior appearing in other unwhitened DFSE schemes is eliminated. The simulation results indicate that a substantial performance improvement over the MMSE equalizer can be achieved either by using Comb. LE-MUDFSE or whitened DFSE (WDFSE) 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".