Space-Time Continuous Phase Modulation for Non-Coherent Detection
Bibliographic record
Abstract
The combination of space-time (ST) coding and continuous-phase modulation (CPM) is attractive for power- efficient transmission over fading channels. In this paper, we propose a diagonal block-based ST-CPM (DBST-CPM) scheme, which in addition to its high power efficiency facilitates noncoherent detection at the receiver. DBST-CPM can be regarded as a non-trivial extension of well-known differential ST modulation (DSTM) with diagonal signal matrices for linear modulation formats. For optimization of the code parameters of DBST-CPM we derive an upper bound on the frame-error rate of DBST-CPM in the quasi-static fading channel (QSFC) and we present an efficient optimization algorithm. Additionally, we derive decision rules for low-complexity non-coherent detection of DBST-CPM in various fading environments. Numerical and simulation results show that (a) the derived upper bound accurately predicts the performance of DBST-CPM in the QSFC, (b) the proposed code optimization yields highly power-efficient designs, and (c) the non-coherent detectors approach the performances of their coherent counterparts for various fading channel models.
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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 | 0.001 |
| 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".