Optimizing Forward Error Correction Codes for COFDM With Reduced PAPR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Coded orthogonal frequency-division multiplexing (COFDM) is a popular modulation technique for wireless communication that guarantees reliable transmission of data over noisy wireless channels. However, a major disadvantage in implementing it is its resulting high peak to average power ratio (PAPR). Including forward error correction (FEC) in the orthogonal frequency division multiplexing (OFDM) system enables the avoidance of transmission errors. Nevertheless, the selected code may impact the value of PAPR. The objective of this paper is to analyze the impact of FEC on the PAPR for the COFDM system based on the autocorrelation of the signal, before the inverse fast Fourier transform (IFFT) block in the COFDM system, the evaluation of the complementary cumulative distribution function (CCDF) of PAPR, and the bit error rate (BER). The autocorrelation of the COFDM system is calculated based on a Markov chain model. From the results, we can reach a conclusion on the characteristics we need to consider in order to choose the codes relating to the PAPR performance in the COFDM system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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