On joint detection and channel estimation over rank-deficient MIMO links with sphere decoding
Bibliographic record
Abstract
In this paper, we present a method which jointly performs robust channel estimation and signal detection over rank-deficient MIMO links. This technique achieves an excellent estimation of the channel matrix by using all detected symbols within a data frame. Symbol detection is achieved by means of a sphere decoding algorithm. The proposed scheme is equivalent to having the training preamble occupy the entire data frame. The Mean Square Error (MSE) of the channel estimation for this method is close to 10-3for 16-QAM modulation over a wide range of preamble lengths. The approach is applicable in both full rank (N = M) and rank-deficient (N <; M) channels with practically the same performance as that of a clairvoyant detector with perfect CSI knowledge. This characteristic is crucial when co-channel signals are sent over an underdetermined channel, where there are more antennas at the transmitter than there are at the receiver, i.e. virtual MIMO processing.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".