A Comparison of DCT-Based OFDM and DFT-Based OFDM in Frequency Offset and Fading Channels
Bibliographic record
Abstract
A precise method for calculating the bit-error probability (BEP) of a discrete cosine transform (DCT)-based orthogonal frequency-division multiplexing (OFDM) system on additive white Gaussian noise (AWGN) channels in the presence of frequency offset is derived. These accurate results are used to examine and compare the BEP performance of a DCT-OFDM system and the conventional discrete Fourier transform (DFT)-based OFDM system in an AWGN environment. Several signaling formats, such as binary phase-shift keying, quaternary phase-shift keying, and 16-ary quadrature amplitude modulation are considered. The performance of a DCT-OFDM with a zero-padding guard-interval scheme is then compared with a zero-padded DFT-OFDM with the employment of minimum mean-square error (MMSE) detection and MMSE decision-feedback detection with ordering scheme over frequency-selective fast Rayleigh fading channels. Analysis and simulation results show that the DCT-OFDM system outperforms the DFT-OFDM system in the presence of frequency offset, and in frequency-selective fast-fading environments
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".