Multi-user decision-feedback space-time processing with partial cross-feedback connectivity
Bibliographic record
Abstract
This paper investigates space-time receiver architectures in multi-user wireless systems where optimal idealized (infinite-length) space-time filtering is applied to minimize the mean-square error. Both feedforward, decision-feedback and cross-decision feedback filters are employed; it follows that not only does the system eliminate the post-cursor ISI (intersymbol interference) but also some portion of the post-cursor CCI (co-channel interference). Such a receiver is most useful in the context of an SDMA (space division multiple access) system since the base station then has readily available knowledge on the decisions of the in-cell co-channel interfering signals. However, significant CCI is also received from outside the cell for which there is normally no decision information available. Therefore, in-cell and out-of-cell co-channel interferers will be treated differently by the receiver since no cross-feedback filter can be implemented for the out-of-cell signals. Our analysis leads to a closed-form expression for the minimum achievable MSE (mean-square error) for both fully- and partially-connected cross-decision feedback systems (XDF). Numerical results compare the performance of XDF systems with standard space-time DF and linear processing as well as the matched-filter bound.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".