Downlink multi-user interference alignment in two-cell scenario
Bibliographic record
Abstract
In this paper, the problem of Downlink Multi-User MIMO (DL MU-MIMO) transmission from two interfering transmitters, each equipped with M antennas to multiple users each equipped with K antennas is considered. It is assumed that all users receive a single data stream of rank one from only one of the transmitters. A novel transmission/reception scheme is proposed based on the idea of Interference Alignment (IA), which aligns the interference coming from each transmitter to the users in the other cell along a single predetermined vector vref, and hence, leaves more degrees of freedom for signal transmission from each transmitter. Furthermore, unlike other IA-based schemes in the literature, only local Channel State Information (CSI) is required at nodes. It is shown that for the case of K ≥ M, the total degrees of freedom of 2M - 2 is achievable. The proposed scheme is also extended to the case of K <; M based on the ideas of Euclidean distance minimization and time/frequency extension. Finally, simulation results are provided to compare the performance of the proposed scheme with that of the existing results in the literature.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".