Joint Carrier Frequency Offset and Doubly Selective Channel Estimation for MIMO-OFDMA Uplink With Kalman and Particle Filtering
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
This paper presents two novel approaches for joint carrier frequency offset (CFO) and doubly selective channel estimation in the uplink of multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) systems. Considering high-mobility situations, where channels change within one OFDMA symbol interval, and the time varying nature of CFOs, basis expansion modeling (BEM) is employed to represent the time variations of the channel. Two new approaches are then proposed based on Schmidt-Kalman filtering (SKF). The first approach utilizes Schmidt-extended Kalman filtering for each user to estimate CFO and BEM coefficients. The second approach uses Gaussian particle filtering along with SKF to estimate CFO and BEM coefficients of each user. The Bayesian Cramér-Rao bound is derived, and the performances of the new schemes are evaluated using the mean square errors. It is demonstrated that the new approaches can significantly improve the mean-square error performance in comparison with the existing methods.
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