Bibliographic record
Abstract
Irregular low-density parity-check (LDPC) codes have shown exceptional performance for single-antenna systems over AWGN channels. In this paper, we investigate their application in multiple-input multiple-output (MIMO) systems over flat quasi-static Rayleigh fading channels. MIMO systems, with multiple antennas at both ends of the communication link, promise high capacity in theory [G.J. Foschini et al., March 1998], [E. Telatar, Nov. 1999], when the receiver has perfect knowledge of the channel state information. In order to exploit such high capacity with manageable receiver complexity, the Bell Labs layered space-time architecture (BLAST) and its variants have been proposed [(G.J. Foschini, Aug. 1996), (V. Tarokh et al., May 1999), (H.E. Gamal et al., Sept. 2001], which transmit independent bit streams on each layer (formed of selected transmit antennas for every time slot). In this paper, we study the diagonal BLAST structure with LDPC coding as the component code of each layer, employing iterative parallel MMSE (minimum mean square error) interference cancellation and detection [H.E. Gamal et al., Sept. 2001]. We show by simulation that LDPC codes can significantly outperform convolutional codes in the same layered structure, even for a very short frame containing 130 space-time symbols.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".