Performance of convolutionally-coded MIMO systems with antenna selection
Bibliographic record
Abstract
In this work, we study the performance of a serial concatenated scheme comprising a convolutional code (CC) and an orthogonal space-time block code (STBC) separated by an inter-leaver. Specifically, we derive performance bounds for this concatenated scheme, clearly quantify the impact of using a CC in conjunction with a STBC, and compare that to using a STBC code only. Furthermore, we examine the impact of performing antenna selection at the receiver on the diversity order and coding gain of the system. In performing antenna selection, we adopt a selection criterion that is based on maximizing the instantaneous signal-to-noise ratio (SNR) at the receiver. That is, we select a subset of the available receive antennas that maximizes the received SNR. Two channel models are considered in this study: Fast fading and quasi-static fading. For both cases, our analyses show that substantial coding gains can be achieved, which is confirmed through Monte-Carlo simulations. We demonstrate that the spatial diversity is maintained for all cases, whereas the coding gain deteriorates by no more than 10 log10(M/L) dB, all relative to the full complexity multiple-input multiple-output (MIMO) system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".