A New Transceiver Architecture for Multilayered Space-Time Coded MIMO Systems
Bibliographic record
Abstract
In this paper, we propose a new transceiver architecture for multiple-input multiple-output (MIMO) systems. The proposed scheme borrows ideas from the multilayered space-time coding (MLSTC) and threaded space-time coding (TSTC) schemes in an effort to maximize the diversity order while maintaining a low complexity detection. Specifically, the proposed scheme has a structure similar to that of the MLSTC while it employs a spatial interleaver (SI) in front of the MLSTC encoder. We also consider a low-complexity decoder that is based on the minimum mean squared error (MMSE) criterion and the QR decomposition (QRD). We provide analytical and simulation results through which we demonstrate that the proposed scheme provides improved diversity-multiplexing gain trade-off, as well as improved performance, all relative to the MLSTC scheme. In addition, with the proposed scheme, all layers achieve the same performance, which makes it attractive in multiuser environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".