Two-Dimensional DPS-BEM Based Channel Estimation for MIMO OTFS Systems
Bibliographic record
Abstract
In this paper, a novel doubly selective channel estimation (CE) scheme is proposed for multiple-input multiple-output (MIMO) orthogonal time frequency space (OTFS) systems. Using the two-dimensional (2D) discrete prolate spheroidal basis expansion model (DPS-BEM), a new analytical representation of the MIMO channel is derived in the delay-Doppler domain. This results in a significant reduction of the number of unknown parameters required for CE compared to methods that directly estimate the channel. Moreover, inspired by the pilot structure in recent works, a new low-overhead pilot scheme is introduced which is capable of effectively capturing the channel's temporal variations and enhancing the estimation performance of the proposed method. The accuracy of the proposed 2D DPS-BEM CE method is evaluated by deriving the theoretical Cramer-Rao lower bound, which shows a low estimation error. Also, the proposed method's computational complexity is calculated and compared with other recent techniques, demonstrating lower complexity. Simulation results further validate that the proposed method outperforms the existing CE approaches in terms of normalized mean squared error and bit error rate, verifying the effectiveness of the proposed 2D DPS-BEM MIMO channel representation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".