Selective Mapping OFDM without Side Information Using a Low Complexity ML Decoder
Why this work is in the frame
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Bibliographic record
Abstract
Selective mapping (SLM) has been proposed for peak to average power reduction (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. With SLM, multiple sequences are generated by multiplying independent phase sequences with the original data and the sequence with the lowest PAPR is chosen for transmission. Hence, to determine the selected sequence at the receiver, side information must be sent along with the data. Previously, a maximum-likelihood (ML) decoder was proposed to eliminate this side information. However, this adds significant complexity to the receiver. In this paper, we first propose a partial SLM (P-SLM) technique using combinations of time-domain sequences. We then show that P-SLM can significantly reduce the ML decoder complexity. We examine the bit error rate (BER) performance of the simplified decoder over additive white Gaussian noise (AWGN) and fading channels. The results show that the proposed decoder has almost identical BER performance to that of the previous decoding algorithm while achieving very low computational complexity.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it