New Algorithms for Peak-to-Mean Envelope Power Reduction of OFDM Systems through Sign Selection
Bibliographic record
Abstract
It has been shown that for multi-carrier signals with \n subcarriers, the peak-to-mean envelope power ratio (PMEPR) of a random codeword generated from a symmetric spherical, QAM or PSK constellation is \log(n) asymptotically. Motivated by this result, recently a coding scheme with a rate of 1-\log_q(2) over a symmetric q-ary constellation has been proposed that achieves a PMEPR less than c \log(n), where c is a constant. The idea of this coding scheme is to adjust the sign of subcarriers using so-called Chernoff bound-based derandomization algorithm. In this paper, using Chernoff bound and second order exponential Markov bound in conjunction with Gaussian approximation, two new variations of the derandomization algorithm are presented that yield roughly the same statistical PMEPR at the same rate. Moreover, it is rigorously established that the asymptotic PMEPR of both these algorithms is exactly the same as that of the original derandomization algorithm. Given a fixed amount of memory, our new algorithms can reduce the complexity up to one order, i.e. from \O(n^3) to \O(n^2). On the other hand, given a fixed computational complexity, our algorithms can reduce the required memory down to half.
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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 | 0.002 | 0.008 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".