Blind Recursive Subspace-Based Identification of Time-Varying Wideband MIMO Channels
Bibliographic record
Abstract
We present a blind recursive algorithm for tracking rapidly time-varying wireless channels in precoded multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. Subspace-based tracking is normally considered for slowly time-varying channels only. Due to the frequency correlation of the wireless channels, the proposed scheme can collect data not only from the time but from the frequency domain as well to speed up the update of the required second-order statistics. After each such update, the subspace information is recomputed using the orthogonal iteration, and then, a new channel estimate is obtained. We also investigate choices of precoder in terms of the tradeoff between the symbol recovery capability and the channel estimation performance and demonstrate the convergence properties of our approach. The proposed algorithm is evaluated in a Third-Generation Partnership Project (3GPP) Spatial Channel Model suburban macro scenario, in which a mobile station is allowed to move in any direction with a speed up to 100 km/h, corresponding to a maximum Doppler shift of about 230 Hz in this case. Numerical experiments show that the normalized mean square error of the channel estimates converges to a level of -30 dB within less than five OFDM symbols when the signal-to-noise ratio (SNR) (per symbol) is ≥ 20 dB.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".