Evaluation of orthogonal frequency‐division multiple‐access systems in the presence of carrier frequency offset using wireless images transmission
Bibliographic record
Abstract
Abstract Orthogonal Frequency‐Division Multiple‐Access (OFDMA) is used in different recent wireless communication systems. However, it is susceptible to carrier frequency offset (CFO) which effects the orthogonality between subcarriers and causes inter‐carrier interference (ICI) and multiple access interference (MAI). The aim of this paper is to analyze the impact of the CFOs on the performance of Discrete Sine Transform (DST) DST‐OFDMA, Discrete Cosine Transform (DCT) DCT‐OFDMA and Discrete Fourier Transform (DFT) DFT‐OFDMA systems and to evaluate them with different modulation schemes and different subcarriers mapping schemes over vehicular A and Stanford University Interim (SUI) 3 channels. Transmission of image over the wireless system is performed to analyze and investigate the performance in terms of the Peak Signal‐to‐Noise Ratio (PSNR), and the Mean Square Error (MSE). Through the evaluation, with CFO compensation, without CFO, and without CFO compensation cases are considered. In this work, Joint Minimum Mean Square Error (JMMSE) equalization and CFO compensation technique is used. It is noted that when the QPSK scheme and the SUI3 channel model are used then the DCT‐OFDMA system provides better MSE and PSNR performances than other systems for the JMMSE compensation technique.
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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 | 0.002 |
| 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) | 0.001 | 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".