Uplink scheduling in multi-cell MU-MIMO systems with ZF post-processing and diversity combining
Bibliographic record
Abstract
This paper considers the selection of users and corresponding uplink decoding in multi-user multiple-input multiple-output (MU-MIMO) multi-cell systems, applicable to future wireless networks with centralized processing at the wireless controller (WC). Two-layer decoding is proposed. First, for all active mobile stations (MSs) the spatial streams are decoupled at the pre-assigned base stations (BSs) by using a zero-forcing (ZF) type algorithm. Second, for MSs with strong signals, hard-decision decoding is performed at the BSs; while for other MSs, especially those at the edge of the cells, soft decisions from multiple BSs are passed to the WC, where maximum ratio combining (MRC) is performed. When deploying MRC, two strategies are considered to deal with the inter-cell interference (ICI). Assuming access at the WC to hard-decoded user data, or the lack of it, MRC with successive interference cancellation (SIC) and conventional MRC are investigated, respectively. Simulation results are provided to demonstrate the potential of the technique developed in terms of total system sum rate performance.
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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 | 0.001 |
| 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 | 0.000 | 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".