MIMO-Based Chirp Spread Spectrum With Permutation Matrix Modulation
Bibliographic record
Abstract
In this article, we propose a multiple-input-multiple-output (MIMO) configuration for chirp spread spectrum (CSS) modulation integrated with the permutation matrix modulation (PMM) scheme, namely, MIMO-CSS-PMM. The proposed MIMO-CSS-PMM simultaneously improves the spectral efficiency (SE) and error performance of the CSS-based transmission scheme. For the detection, we formulate the optimum maximum-likelihood (ML) detector that turns out to be of high computational complexity. We propose two low complexity semi-coherent detection schemes, i.e., scheme I and scheme II, in which we average over the fast Fourier transform (FFT) output of the dechirped signal of each receiver antenna. Then, in scheme I, we reduce the ML search set by selecting the number of largest averaged signal values corresponding to the number of transmitter antennas. To further reduce the complexity of scheme I, in scheme II, we define and derive a probability of detection using concepts from order statistics and find a threshold value maximizing this probability of detection. We select the number of the largest averaged signal greater than or equal to the obtained threshold to eliminate the unnecessary cases from the search set of scheme I. With the help of computer simulations, we evaluate the proposed detectors in terms of bit error rates (BERs).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".