Downlink Scheduling for Multiple Antenna Systems with Dirty Paper Coding Via Genetic Algorithms
Bibliographic record
Abstract
MIMO systems are of interest to meet the expected demands for higher data rates and lower delays in future wireless systems. The introduction of multiple transmit antennas adds additional complexity to any scheduling algorithm for the multi-user system. It is optimal to transmit to multiple users simultaneously in contrast to a single user in a single-input single-output (SISO) system, resulting in a combinatorial optimization problem. In this paper, we analyze the performance of scheduling through utility functions implemented via a genetic algorithm. Namely, we investigate the maximum throughput and the proportionally fair utility functions. The analysis is in the context of a MIMO broadcast channel using dirty paper coding (DPC). This paper builds upon earlier work using zero-forcing beamforming instead of DPC. Under DPC, the order of user encoding affects the user data rates and hence the performance of the scheduling algorithm. We demonstrate that the genetic algorithm is able to achieve a near-optimal performance relative to an exhaustive search at a significant reduction in computational complexity.
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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".