Linear Dispersion for Single-Carrier Communications in Frequency Selective Channels
Bibliographic record
Abstract
Linear dispersion coded orthogonal frequency division multiplexing (LDC-OFDM) has recently been proposed to improve joint frequency and time diversity. This paper investigates whether LDC are able to support joint frequency and time diversity for single-carrier block communications in time-varying frequency selective fading channels, and proposes linear dispersion coded cyclic-prefix single-carrier modulation (LDC- CP-SCM), which utilizes LDC across multiple CP-SCM blocks. LDC-CP-SCM uses a layered two-stage LDC decoding strategy, and is thus backwards-compatible to CP-SCM systems. This paper analyzes the diversity properties of LDC-CP-SCM, and provides a sufficient condition for LDC-CP-SCM to maximize all available joint frequency and time diversity gain and coding gain. For the LDC considered, simulations show that with and without carrier frequency offset (CFO) effects, LDC-CP-SCM may outperform both CP-SCM and LDC-CP-OFDM in time- varying frequency selective channels. This paper also shows that LDC-CP-SCM with forward error correction (FEC) may outperform CP-SCM with FEC over time.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".