Channel Estimation and Symbol Detection for Communications on Overlapping Channels
Bibliographic record
Abstract
In this paper, we investigate the joint channel estimation, interference cancellation and data detection problem for a general setting in which a desired communication is interfered by another communication having a different symbol rate and being asynchronous with the considered communication. The fast fading channel gains of the desired communication and the effective interference coefficients (EIC) induced by filters of the interfering signal must be estimated to enable reliable detection of the desired data. However, the joint estimation of fast fading channel and EIC from communications with different bandwidths has not been studied in literature. Toward this end, we propose a two-phase strategy for channel estimation and data detection. In the first phase, we derive the closed-form maximum-likelihood estimator of the EIC. Then, the interference is subtracted and the desired channel gains corresponding to pilot symbols are estimated. In the second phase, with the knowledge of desired channel information obtained from the previous phase, we derive the posterior probability for data symbols for the soft data detection. Via numerical studies, we demonstrate that our design can effectively cancel the interference and the soft detection approach can achieve better symbol error rate compared to the existing message passing based detection approach. We show that our design can perform well for a wide range of interference power and frequency spacing and it has lower complexity than the existing technique.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".