Zero-Forcing Assisted RFMD Detection of SE-MOCZ for Non-Coherent Short Packet Communications
Bibliographic record
Abstract
This paper investigates the detection of spectrally-efficient modulation on conjugate reciprocal zeros (SE-MOCZ) as a promising non-coherent modulation for short packet communications (SPCs). SE-MOCZ combines the recently proposed MOCZ and faster-than-Nyquist (FTN) signaling, by accelerating the pulses carrying the polynomial coefficients of MOCZ beyond the Nyquist limit, which introduces inter-polynomial-coefficient-interference (IPCI) and increases the number of the received complex zeros in the z-domain. We propose to use zero forcing (ZF) to assist the root-finding-minimum-distance (RFMD) detector of SE-MOCZ without the need to optimize the radius or to use the partial-complex-zeros-removal filter. In particular, given that the complex zeros due to the IPCI are fully known at the receiver, ZF can cancel such complex zeros, and hence, assist the RFMD detection. Simulation results show that the proposed ZF-RFMD detector outperforms the RFMD detector that adopts optimal radius and partial-complex-zeros-removal filter (up to 4 dB saving in Eb/N0at the same error rate). Additionally, the proposed ZF-RFMD detector helps SE-MOCZ to approach the performance of MOCZ for high values of the FTN signaling acceleration parameter (up to 8.9% SE gain with no additional increase in Eb/N0).
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".