Training sequence design for robust joint detection and channel estimation over rank-deficient MIMO links
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Bibliographic record
Abstract
In this paper, we present a study of optimal training sequences for robust joint channel estimation and signal detection. Particularly, we study the case of virtual MIMO links, where there are more co-channel signals M than receive antennas N, i.e., N <;M. A training sequence based on the one-dimensional chaotic Chebyshev map is presented herein. This sequence delivers robust performance in terms of Bit Error rate (BER), Normalized Mean-Squared-Error (NMSE) of the estimation and computation complexity. The proposed sequence exhibits an optimal performance by spanning only a minimal number of training symbols, i.e., L=M. The proposed chaotic-based training sequence performs adequatly on both i.i.d. and correlated Rayleigh Fading Channels without the need for a priori statistics of the channel.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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