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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".