Robust transceiver design for geometric mean decomposition systems with limited precoder feedback
Bibliographic record
Abstract
With the assumption of a slow-time-variant multiple-input multiple-output (MIMO) channel, the use of channel state information at the transmitter coupled with the use of a jointly optimized linear transceiver can achieve excellent performance. The geometric mean decomposition (GMD) when combined with BLAST detection can provide the same diversity order as a more complex ML detector without sacrificing the rate of the MIMO system. The main obstacle to the practical implementation of this scheme is whether or not it performs well when the transmitter has limited feedback. In this paper we propose a decoder designed to be robust against quantization errors, as well as a quantizer that reduces the number of required feedback bits to approximate the performance of GMD when it has infinite feedback for an N times M MIMO system. Our results show that we can reduce the amount of feedback bits from 64 to 10 bits for a 2 times 2 and require only 30 bits for a 3times3 MIMO system, while achieving performance nearly identical to infinite feedback case.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".