Complexity reduction of PTS technique to reduce PAPR of OFDM signal used in a wireless communication system
Bibliographic record
Abstract
A new partial transmit sequence (new‐PTS) scheme is illustrated in this study. The target behind it is to reduce the peak‐to‐average power ratio (PAPR) in an orthogonal frequency division multiplexing (OFDM) system. Despite its competitive attributes, the PTS technique is considered computationally expensive due to multiple inverse fast Fourier transforms (IFFT) and the need of a thorough investigation to find the optimal phase factor. The primary concern, thus, is to eliminate the IFFT blocks. In the present study, a remarkable strategy has been followed, mainly relying on analysing the available data in the random access memory. Additionally, the least PAPR value is calculated and its corresponding address is precisely determined; such an address is the side information to be sent to recover the users' original data at the OFDM receiver. Moreover, the effectiveness of the so‐called complexity reduction of the new‐PTS method is pointed out in order to limit the number of searches that are required to acquire the best PAPR performance, which significantly reduces the computational complexity overhead. Consequently, the numerical analysis and comparative study show the overall high performance, which the proposed PTS scheme offers in respect to both the bit error rate and PAPR reduction.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".