An incremental Grassmannian feedback scheme for linearly precoded spatial multiplexing MIMO systems
Bibliographic record
Abstract
An effective strategy for transmitter adaptation on slow block-fading multiple-input multiple-output (MIMO) links is for the receiver to inform the transmitter of the subspace over which transmission should take place, and for the transmitter to allocate power uniformly over that subspace. The design of a feedback scheme to implement this strategy can be viewed as a (lossy) source compression problem on a Grassmannian manifold. Memoryless vector quantization on each fading block is one approach to that problem, but it neglects any correlation between blocks. In some recent work, several approaches have been proposed to take advantage of this correlation. In this paper we propose an alternative technique that leverages existing Grassmannian codebooks from memoryless schemes and employs a quantized form of geodesic interpolation. Distinguishing features of the proposed technique include the fact that it only requires a single codebook, and the fact that it enables the step length of the geodesic interpolation to be adapted to the channel realization, rather than the channel statistics. In some straightforward simulation experiments, the proposed approach provides better performance than an existing scheme.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".