Closed-Loop Transmit Diversity with Imperfect Feedback
Bibliographic record
Abstract
In the closed-loop transmit diversity systems, feedback delay and feedback error, as well as the sub-optimum reconstruction of the quantized feedback data, are the usual sources of deficiency. We address the efficient reconstruction of the beamforming weights in the presence of the feedback imperfections, by exploiting the residual redundancies in the feedback stream. We propose two approaches to improve the performance. One is based on using a channel predictor at the receiver to compensate for the delay. Another approach deals with the feedback imperfections in a unified reconstruction algorithm using JSCC techniques. Furthermore, we introduce the concept of Blind Antenna Verification (BAV). The closed-loop Mode 1 of the 3GPP standard is used as a benchmark, and the performance is examined within a Wideband-CDMA simulation framework. It is demonstrated that the proposed algorithms outperform the standard at all mobile speeds, and are suitable for the implementation in practice.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".