Faster-than-Nyquist Single-Carrier MIMO Signaling
Bibliographic record
Abstract
Faster-Than-Nyquist (FTN) signaling is created when a digital communications signal has an occupied bandwidth less than the symbol rate. It has been previously demonstrated that FTN signals can achieve bit error rates near that of full-bandwidth signaling for BPSK and 4-QPSK modulation. Iterative frequency domain equalization allows for low cost receivers. This paper presents FTN signaling for Multiple-Input/Multiple-Output (MIMO) signals where a Single-Carrier (SC) MIMO signal is converted to the frequency domain and its occupied bandwidth is reduced by removing some of the frequency components before conversion back to the time domain. A previously proposed Single-Input/Single-Output FTN iterative receiver algorithm is extended for FTN SC-MIMO signals. Singular Value Decompositions (SVD) are proactively performed on the channel transfer matrices to reduce the computation cost of the iterative receiver. It is demonstrated that the Bit Error Rate (BER) performance of this MIMO FTN scheme is near that of full bandwidth MIMO signals with a low cost receiver algorithm. The signalling is robust to the selection of the frequency components that are nulled.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".