Improved Closed-Loop Communication in the Presence of Feedback Delay and Error
Bibliographic record
Abstract
The closed-loop transmit diversity technique is used to improve the performance of the downlink channel in MIMO communication systems. The WCDMA standard endorsed by 3GPP adopts two modes of downlink closed-loop schemes based on partial channel state information. The information is fed back from the mobile unit to the base station through a low-rate uncoded feedback bit stream. Previously it was addressed the efficient reconstruction of the beamforming weight in the presence of feedback error, with the constraint of a constant transmit power. In this article, the issue of feedback delay is also considered. Using joint source-channel coding techniques, a reconstruction algorithm is introduced to improve the performance of mode 1 of 3GPP in the presence of feedback error and delay, by taking advantage of the redundancy available in the bitstream of channel state information. We also introduce the novel concept of blind antenna verification. It can substitute the conventional antenna weight verification process without the need to any training data. The performance is examined within a simulated 3GPP framework. It is demonstrated that the proposed algorithms have substantial gain over the conventional method for low, moderate and high mobile speeds. The proposed approaches are applicable to other feedback schemes as well.
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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 | 0.000 |
| 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) | 0.001 | 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".