Sparse Channel Estimation utilizing Optimal Wiener-Hopf Filtering in MIMO-OTFS Paradigm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article focuses on the MIMO-OTFS system paradigm and analyzes its three-dimensional clustering sparse characteristics based on the convolution process of signals and dual dispersion fast time-varying channels. A iterative parameter estimation algorithm based on Wiener-Hopf optimal filtering was designed in the Delay-Doppler-Angle (DDA) domain, which is the Segmented Orthogonal Matching Tracking Scheme with the assistance of minimum mean square error iteration (MAI StOMP). The performance of the proposed algorithm was compared with that of the previous proposed algorithm, and numerical simulations were conducted. The results showed that the algorithm proposed in this paper has good Normalized Mean Square Error (NMSE) performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it