Channel Coding Rate for Finite Blocklength Faster-Than-Nyquist Signaling
Bibliographic record
Abstract
The fundamental tradeoff between low latency and high reliability makes the design of ultra-reliable low-latency communications (URLLC) wireless systems challenging. To support URLLC for a fixed bandwidth, faster-than-Nyquist (FTN) signaling is a promising approach since it increases the degrees of freedom (i.e., channel uses) per time interval, which can be exploited to improve reliability. In this letter, we derive analytical expressions for the approximate maximum channel coding rate (MCCR) for finite blocklength FTN signaling for water-filling and equal power allocations. We show that for practical non-sinc square-root Nyquist pulses, the penalty on the rate incurred due to the finite blocklength can be significantly reduced by non-orthogonal FTN transmission. Our results reveal that the MCCR for finite blocklength FTN signaling exceeds the Shannon capacity achieved for infinite blocklength and orthogonal transmission.
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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 | 0.007 |
| 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) | 0.002 | 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".