Channel Estimation for Filtered OFDM Transceiver Systems
Bibliographic record
Abstract
The advantages of filtered orthogonal frequency division multiplexing (f-OFDM) over conventional OFDM, universally filtered multicarrier (UFMC) and filter bank multi-carrier (FBMC) techniques have made it a prominent choice for the future generations of wireless networks. Nevertheless, due to its backward compatibility, the specific task of channel estimation for f-OFDM systems has not yet been addressed; although, due to the difference in the signal model, by doing so one might achieve improvements in the performance or spectral efficiency of the system. We develop a pilot-aided channel estimation scheme for f-OFDM with no or minimal cyclic prefix (CP) based on the statistical properties of the interference, i.e. inter-symbol interference (ISI), inter-carrier interference (ICI), adjacent-carrier interference (ACI) and noise terms. We demonstrate the possibility to shorten or totally remove the CP from the f-OFDM transceiver while applying a one-tap-per-subcarrier equalizer and maintaining the performance and complexity of the transceiver system satisfactory. We propose a pilot-based least square (LS) estimator and an average-taking modified variant of it that perform very close to the perfect channel and outperform a pilot-to-pilot interpolation-based channel estimator in BER simulations.
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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 | 0.001 |
| 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".