Design of space-time trellis codes with multi-H CPM signals
Bibliographic record
Abstract
Signal design for wireless data links modeled as quasi-static Rayleigh is approached using space-time trellis (STT) coding and power/bandwidth efficient multi-h phase-coded continuous phase modulation (CPM) signals. System configuration for achieving multiple-input multiple-output (MIMO) with arbitrary number of antennas at the transmitter and receiver is described. At the receiver, the received signal is modeled as a linear sum of Rayleigh faded multi-h CPM signals with additive white Gaussian noise (AWGN). The problem of recovering transmitted data from such a composite signal is addressed using the criterion of maximum likelihood sequence estimation (MLSE). The finite-state discrete-time properties of the received composite signal are exploited and using Euclidean distance criterion best STT codes are determined. Performance analysis is presented based on pair-wise error probabilities (PWEP) for all possible pairs of signals in signal-space. It is noted that PWEP is a function of signal matrix, hence on multi-h CPM, and channel gain matrix. The signal matrix corresponding to error events is presented as a function of multi-h CPM signal parameters. From this signal matrix, the rank and the minimum determinant (minimum product of eigenvalues) of STT coded multi-h CPM can be found. It is shown that for these STT codes coding gain of 1.9 dB can be achieved relative to identical coding and MSK.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".