User Selection for MU-MIMO Based on Channel Estimation and Spatial Orthogonality
Bibliographic record
Abstract
This paper addresses interference management in downlink (DL) transmission of a multi-user multiple-input multiple-output (MU-MIMO) system in time division duplex (TDD) mode. A novel method of user selection is proposed based on a relaxed definition of spatial orthogonality, where the base station (BS) obtains channel state information (CSI) using pilot symbols in uplink (UL) transmission. Due to channel reciprocity in TDD mode, the BS selects user groups based on obtained CSI and maximum pairwise channel orthogonality for the DL transmission. Then, the BS employs linear beamformers including maximum ratio transmission (MRT) and zero forcing (ZF) to transmit the payload symbols for the users. Analytical and simulated results indicate the accuracy of the proposed method in terms of the achievable rate. Also for the proposed user selection method, the optimal training sequence length in UL channel is found to maximize sum rate among the selected users.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".