User-Specific Channel Estimation Overhead Optimization and Resource Allocation for Multi-User OTFS Systems
Bibliographic record
Abstract
Accurate channel estimation is one of the major challenges in deploying orthogonal time frequency space (OTFS) systems, because the inter-grid interference (IGI) between the pilot and data caused by multi-path channels significantly reduces estimation accuracy. Existing solutions embed the unified guard zero-symbols to prevent IGI in multi-user OTFS systems, but they ignore that users have varying abilities to mitigate IGI based on their specific channel conditions. Consequently, using the same guard for different users leads to redundant guard symbols, which reduces spectrum efficiency. In this letter, we leverage user-specific statistic channel characteristics to design a tailored channel estimation overhead optimization and resource allocation scheme to enhance the spectrum efficiency for multi-user OTFS systems. Specifically, we first derive the mathematical expression of the transmission capacity. Then we formulate a total capacity maximization problem by jointly optimizing the channel estimation overhead and bandwidth, subject to individual rate requirements. To solve this non-convex problem, we introduce an alternative optimization algorithm and derive closed-form expressions for updating the solutions in each iteration.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".