Phase Noise Compensation for CFBMC–OQAM Systems Under Imperfect Channel Estimation
Bibliographic record
Abstract
Among many multi-carrier systems, circular filter-bank multi-carrier offset quadrature amplitude modulation (CFBMC-OQAM) is one of promising candidates for future wireless communications. This paper studies the impact of phase noise and its compensation for CFBMC-OQAM under imperfect channel estimation, which has not been done before. In the presence of phase noise, a two-stage phase noise compensation algorithm is proposed. In the first stage, the channel frequency response and phase noise are estimated based on the transmission of a preamble. Such a preamble is designed to minimize the channel mean squared error. In the second stage, the estimated channel obtained from the first stage together with pilot symbols are used to compensate for the phase noise and detect the transmitted signal. Simulation results obtained under practical scenarios show that the proposed algorithm effectively estimates the channel frequency response and compensates for the phase noise. The proposed algorithm is also shown to outperform an existing algorithm that performs iterative phase noise compensation when phase noise impact is high.
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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 | 0.002 |
| 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 | 0.001 |
| 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 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".