Bibliographic record
Abstract
In this paper we consider coherent flat fading wireless communication systems with multiple transmitter antennas and single receiver antenna (MISO). We propose a Toeplitz linear space time block code (STBC) that converts an original MISO flat fading channel into a Toeplitz virtual multiple inputs multiple outputs (MIMO) channel. We show that our proposed code has the following main features: (a) The symbol transmission rate is (T-M + 1)/T, where M is the number of transmitter antenna and T is the number of channel uses (T > M). (b) Linear receivers (zero-forcing and minimum mean square error) can extract full diversity. Moreover, when the channel coefficients are independent and the maximum likelihood (ML) detector is employed, our Toeplitz STBC minimizes the exact worst case average pair-wise error probability, (c) When channels are correlated, we design our Toeplitz STBC that minimizes the exact average worst case pair-wise error probability. By transforming this non-convex optimization problem into a convex one, the problem can be solved efficiently by employing an interior point method. In particular, when the design criterion in question is approximated by the Chernoff bound, we obtain a closed form solution, (d) Finally, for the independent MISO flat fading system, we prove that our proposed codes can approach the optimal diversity-vs-multiplexing tradeoff developed by Zheng and Tse with a linear zero-forcing receiver when the number of channel uses is large
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".