Bibliographic record
Abstract
This paper tackles the problem of precoding and decoding matrices design for a time-division duplexing (TDD) massive MIMO system to support N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</inf> independent data streams. The optimal design requires estimating the top-N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</inf> singular vectors of the high-dimensional channel matrix, which typically involves significant pilot overhead if conventional channel estimation methods are used. Alternatively, some prior works seek to estimate the precoding and decoding matrices directly by exploiting channel reciprocity and the power iteration principle, but their performances suffer in the low SNR regime. To address this issue, this paper proposes a novel active sensing framework, where both transmitter and receiver send pilots alternatingly using their sensing beamformers that are actively designed as functions of previously received pilots. This is accomplished by a proposed active sensing unit, which first employs recurrent neural networks to summarize information from historical observations into their hidden state vectors then uses fully connected neural networks to obtain the sensing beamformers and precoding/decoding matrix. Simulations demonstrate that the proposed method outperforms existing approaches significantly and maintains superior performance even in low SNR regimes.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".