An augmented orthogonal code design for the noncoherent MIMO channel
Bibliographic record
Abstract
This paper deals with the design and detection of structured orthogonal codes for the noncoherent multiple-input multiple-output (MIMO) channel. A code is proposed which increases the rate of so-called noncoherent orthogonal designs by taking a union of permuted noncoherent orthogonal codebooks. For symbols taken from a QPSK constellation, the permuted noncoherent orthogonal codebooks have a strong group structure. This structure can be exploited, using coding, to construct codebooks such that the minimum pairwise codeword distance is no less than that of a noncoherent orthogonal codebook. Furthermore, the codewords of the permuted noncoherent orthogonal codebook maintain the orthogonal structure of the original codebooks so that simple detection is still possible at the receiver. This approach results in an increased data rate without sacrificing the minimum distance. Simulation results are provided for a rate 1.25 bit per channel use (bpcu) permuted noncoherent orthogonal design. This code performs 2 dB worse than a rate 1 bpcu noncoherent orthogonal design with symbols from a QPSK constellation and 4 dB better than a rate 1.5 bpcu noncoherent orthogonal design with symbols from an 8-PSK constellation at a frame error rate of 10-2.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".