Joint synchronization and equalization in the uplink of multi-user OPRFB transceivers
Bibliographic record
Abstract
This paper addresses the problem of carrier frequency synchronization and time-varying channel equalization in the uplink of a broadband multi-user wireless communication system employing an oversampled perfect reconstruction filter bank (OPRFB) transceiver structure for multi-carrier modulation. Based on the maximum likelihood (ML) principle, a pilot-aided joint estimator of the carrier frequency offsets (CFO) and channel equalizer coefficients of the multiple users is proposed. The performance of the new estimator is examined for various subband allocation schemes by means of numerical simulations under realistic conditions of operation. For mobile users with time-varying fading channels, we also study the effect of using different distributions of pilots over time. Our results show that the proposed estimator can provide accurate estimates of the unknown CFO and channel parameters, which in turn can be used to design effective compensation mechanisms.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".