A simplified Diagonal BLAST architecture with iterative parallel-interference cancellation receivers
Bibliographic record
Abstract
We propose a simplified Diagonal-BLAST (D-BLAST) architecture with parallel soft interference cancellation receiver based on the Turbo-BLAST (T-BLAST) architecture. In the T-BLAST architecture, the inter-substream coding is designed by a combination of random space-time interleaving and independent block encoding of each substream, using the same forward-error correction (FEC) code. We show that for the T-BLAST architecture, by using a systematic space-interleaving design that layers each substream diagonally across the antennas, a simplified diagonal inter-substream coding can be achieved without undue implementation complexity. The proposed diagonal inter-substream coding also facilitates the use of an iterative parallel interference cancellation receiver for decoding the simultaneously transmitted data, thereby achieving more capacity compared to the achievable capacity of traditional BLAST (Bell Labs Layered Space Time) architectures using sequential interference cancellation receivers. In this paper, we also present simulation results on fading channels, which confirm these findings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".