Minimum Distance-Based Limited-Feedback Precoder for MIMO Spatial Multiplexing Systems
Bibliographic record
Abstract
Preceding is a well-known method to reach the promised performance and capacity of multiple-input multiple-output (MIMO) systems. Recent investigations, when the transmitter has the channel-state information (CSI), have revealed several preceding techniques. Minimum distance based precoders outperform precoders based on other criteria such as maximizing signal-to-noise ratio (SNR), minimizing the mean square error and maximizing the minimum singular value of the equivalent channel. On the other hand, when the CSI is not available at the transmitter, one resorts to limited feedback precoding methods. Previously, unitary matrices for precoding have been derived from subspace packing in the Grassmann manifold. In this paper, we use the same set of unitary matrices and enhance them by defining the precoder matrix to have a general form not unitary only. We extract the precoding parameters by applying the minimum-distance approach. Although in this case the number of feedback parameters is increased, the performance results are accordingly impressive. The optimality of quantization of feedback parameters is also presented.
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 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) | 0.000 | 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 | 0.001 |
| 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".