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.
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.
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 | 0.001 |
| 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".