Adaptive Space-Time Coding and its Implementation in MIMO Antenna Systems
Bibliographic record
Abstract
The introduction of Multiple-Input Multiple-Output (MIMO) opened the door for space-time processing (STP) techniques to improve the transmission reliably and spectral efficiency. STP algorithms have been mainly proposed to achieve either enhancement in the link quality (through spatial diversity) or throughput gain (through spatial multiplexing). The simultaneous enhancement of link reliability and throughput gain are competing performance objectives. In addition, the performance of either of these methods is highly dependent on the MIMO channel conditions. If the MIMO channel is spatially uncorrelated, it is known to be well conditioned to achieve spatial multiplexing gain. On the other hand, if the MIMO channel is spatially correlated, it is much less able to support spatial multiplexing; hence, performance can be improved through spatial diversity. This paper presents an adaptive layered space-time (LST) processing method. The proposed scheme is shown to be able to adapt to a broad range of MIMO channel environments, providing a significant performance improvement over the conventional non-adaptive STP methods. Furthermore, we discuss some implementation issues of the proposed 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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".