A partial transmit sequence technique with error correction capability and low computation
Bibliographic record
Abstract
SUMMARY Orthogonal frequency division multiplexing (OFDM) is a popular transmission technique in wireless communication. Although already widely addressed in many studies, OFDM still has flaws, one of which is the occurrence of high peak‐to‐average power ratio (PAPR) in the transmission signal. The partial transmit sequence (PTS) technique is one method adopted to reduce high PAPR in OFDM systems. However, as PTS utilizes phase factors to generate multiple candidate signals, large amounts of calculation and time are required to search the candidate signal with the minimal PAPR, which will then be adopted as the final transmission signal. This paper proposes a novel PAPR reduction method, which can be applied in OFDM systems with M‐ary phase‐shift keying modulation. It not only requires less computation but also possesses error correction capabilities. More precisely, the proposed method is to divide a block‐coded modulation code into the direct sum of a correcting subcode for encoding information bits and a scrambling subcode for generating phase factors. Our proposed method is a suboptimal technique with low computation, because it uses a genetic algorithm with a partheno‐crossover operator as the transmitted signal selection mechanism. Simulation results show our proposed method has better PAPR performance than the GA‐PTS scheme. Based on the simulation results in Figures 5 and 6, it is evident that our proposed method can be employed in any OFDM system by using M‐PSK modulation.Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".