Scheduling for MIMO Broadcast Channels with Linear Receivers and Partial Channel State Information
Bibliographic record
Abstract
In multiple-input multiple-output (MIMO) broadcast channels, multiuser diversity is exploited by scheduling data transmission to users with best channel conditions. To find the best set of users, the base station requires knowledge of user channels, which for the case of non-reciprocal uplink and downlink channels will lead to a great increase in feedback overhead. On the other hand, scheduling based on partial channel state information (CSI) often results in a great loss in system throughput. In this paper, a multiuser MIMO technique is presented for MIMO broadcast channels which only requires partial CSI at the base station and achieves a relatively high system throughput. The proposed scheme is a combination of MIMO point-to-point eigenmode transmission with zero-forcing (ZF) zero-forcingat the receivers. Spatial multiplexing is considered and the optimum number of data streams assigned to each of these two schemes in order to maximize the sum-rate is derived. The results show that with a negligible increase in feedback overhead compared to the case where only ZF linear receiver processing is adopted, the proposed scheme leads to a significant increase in the sum-rate.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".