Estimation of transmission line parameters for digital equalization of high-speed data radio
Bibliographic record
Abstract
This work considers the distortion created by an unmatched transmission line system at the receiver of a military data radio. The installation requirements for these types of systems are such that manual tuning of the antenna is impracticable. The antenna impedance may not match that of the cable and radio receiver, resulting in electrical reflections in the cable. These reflections create intersymbol interference (ISI), which distorts the received signal and limits the performance of the communication link. It is shown that this distortion can be modelled using only four parameters: the transit time, the amplitude and the angle of the reflection coefficient and the synchronization offset. A joint maximum likelihood (ML) block estimator for the parameters is presented with the corresponding Cramer-Rao bound. The performance of the estimator is evaluated using simulations and compared to the bound. A more practical iterative estimator algorithm for the joint estimation of the parameters is also suggested. To compensate for the distortion at the receiver, a filter design technique based on the estimated parameters is introduced. The method, obtained from the least squares procedure, produces an approximate inverse filter for the channel, minimizing the distortion at the receiver. Results comparing the proposed method to traditional adaptive equalizers are presented. They show that the minimum mean squared error (MSE) achieved by the proposed method approaches the power of the noise, the minimum value attainable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".