Carrier frequency offset recovery for zero-IF OFDM receivers
Bibliographic record
Abstract
As trends in broadband wireless communications applications demand faster development cycles, smaller sizes, lower costs, and ever increasing data rates, engineers continually seek new ways to harness evolving technology.The zero intermediate frequency receiver architecture has now become popular as it has both economic and size advantages over the traditional superheterodyne architecture.Orthogonal Frequency Division Multiplexing (OFDM) is a popular multi-carrier modulation technique with the ability to provide high data rates over echo ladened channels.It has excellent robustness to impairments caused by multipath, which includes frequency selective fading.Unfortunately, OFDM is very sensitive to the carrier frequency offset (CFO) that is introduced by the downconversion process.The objective of this thesis is to develop and to analyze an algorithm for blind CFO recovery suitable for use with a practical zero-Intermediate Frequency (zero-IF) OFDM telecommunications system.A blind CFO recovery algorithm based upon characteristics of the received signal's power spectrum is proposed.The algorithm's error performance is mathematically analyzed, and the theoretical results are verified with simulations.Simulation shows that the performance of the proposed algorithm agrees with the mathematical analysis.A number of other CFO recovery techniques are compared to the proposed algorithm.The proposed algorithm performs well in comparison and does not suffer from many of the disadvantages of existing blind CFO recovery techniques.Most notably, its performance is not significantly degraded by noisy, frequency selective channels.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".