User Scheduling for Network MIMO Systems with Successive Zero-Forcing Precoding
Bibliographic record
Abstract
In this paper we consider simplified greedy user scheduling algorithms for clustered network multiuser multiple-input multiple-output (MIMO) systems with successive zero-forcing (SZF) precoding. The optimal user scheduling involves an exhaustive search, which is very complex. Among various suboptimal but lower complexity algorithms, greedy algorithms with heuristic scheduling metrics have been shown to achieve performance close to the exhaustive search. In this paper, we propose two simplified algorithms: interference-aware greedy user scheduling (IA-GUS) and interference-whitening greedy user scheduling (IW-GUS). IA-GUS schedules the users based on the information on interference power of the selected users in surrounding cooperating clusters, and IW-GUS schedules the users based on the information on whitened channels of all users requesting the service in the cluster. IA-GUS has lower complexity and performs better than IW- GUS. Simplified metrics for proportionally fair (PF) scheduling are also proposed. Performance of the proposed algorithms is analyzed and compared with dirty paper coding (DPC), block diagonalization (BD) and systems without network coordination. Simulation results demonstrate that the proposed algorithms for SZF achieve much higher outage capacity compared to BD with previously proposed algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".