User Clustering and Hybrid Precoding Design for mm-Wave Multi-User Massive MIMO Systems
Bibliographic record
Abstract
Interference cancellation in hybrid precoded downlink massive multi-user multi-input multi-output (MU-MIMO) systems, e.g., block diagonalization (BD), has a complexity that increases rapidly with the number of users. Spectral clustering into user groups based on mutual interference is proposed to reduce the complexity of servicing a large group of users in the same channel. By assigning users with the lowest mutual interference into separate groups, only intra-group interference cancellation needs to be considered. A relaxed suboptimal user grouping solution is proposed by characterizing MU-MIMO downlink interference using the pairwise angle between channel subspaces as graph weights for spectral clustering. Group assignment is achieved by discretization via constrained spectral clustering. The proposed approach, applied to mmWave multi-user massive MIMO systems, offers new system performance versus complexity trade-offs compared to conventional methods, as demonstrated by numerical results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".