Carrier Frequency Offset Estimation and I/Q Imbalance Compensation for OFDM Systems
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
Two types of radio-frequency front-end imperfections, that is, carrier frequency offset and the inphase/quadrature (I/Q) imbalance are considered for orthogonal frequency division multiplexing (OFDM) communication systems. A preamble-assisted carrier frequency estimator is proposed along with an I/Q imbalance compensation scheme. The new frequency estimator reveals the relationship between the inphase and the quadrature components of the received preamble and extracts the frequency offset from the phase shift caused by the frequency offset and the cross-talk interference due to the I/Q imbalance. The proposed frequency estimation algorithm is fast, efficient, and robust to I/Q imbalance. An I/Q imbalance estimation/compensation algorithm is also presented by solving a least-square problem formulated using the same preamble as employed for the frequency offset estimation. The computational complexity of the I/Q estimation scheme is further reduced by using part of the short symbols with a little sacrifice in the estimation accuracy. Computer simulation and comparison with some of the existing algorithms are conducted, showing the effectiveness of the proposed method.
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 | 0.001 | 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 | 0.001 |
| 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 it