Channel Estimation for a Multi-User System with Iterative Interference Cancelation
Bibliographic record
Abstract
To allow multiple-access between small earth terminals and a satellite hub in a fully unsynchronized manner, the authors present a hub receiver which uses iterative interference cancelation on the physical-layer to separate the traffic. Instead of approaches like Contention Resolution Diversity Slotted ALOHA, which operates on the data-link layer, the receiver presented allows for interference cancelation within one time slot. While the general structure of the hub receiver was already introduced in [7] and [8], the estimation and equalization of channel impairments like sampling-clock offsets as well as carrier frequency offsets are targeted here. The system model is introduced briefly and channel estimation and equalization approaches are presented. For channel estimation the authors recommend a correlation-based channel tap estimator which uses the preamble as well as received and corrected data as pilots for better channel estimation in subsequent iterations. To equalize channel impairments two kinds of single-tap equalizers are investigated. The impact of the estimation and equalization approaches on the bit error rate performance of the system in comparison to previous investigated scenarios where the channel was assumed known are studied [8].
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".