Simplified transmit covariance optimization and user ordering algorithm for successive zero-forcing precoding
Bibliographic record
Abstract
In this paper we consider transmit covariance matrix optimization and user ordering problem for successive zero-forcing (SZF) precoding for multiuser multiple-input multiple-output (MIMO) downlink, where base station as well as the mobile receivers are equipped with multiple antennas. With SZF, an optimization of sum rate maximizing transmit covariance matrices is necessary. A dirty paper coding (DPC) based transmit covariance matrix optimization algorithm has been recently proposed in [1]. The algorithm involves three steps: the dual multiple access channel (MAC) covariance optimization using sum power iterative water-filling [2], the MAC to broadcast channel (BC) covariance transformation [3] for DPC, and transformation of the DPC downlink covariance matrices to SZF matrices. Hence, that algorithm is computationally very complex, and may not be realizable in practice. In this paper, we propose a suboptimal but much simplified algorithm, which employs an iterative procedure similar to the MAC covariance optimization technique of [2], but does not involve multiple levels of covariance matrix transformations. Additionally, the optimized algorithm requires the optimization to be applied to all possible user orders. Hence, we propose a heuristic user ordering algorithm based on previously proposed user selection algorithms, so that the exhaustive search through all possible user orders is avoided without significant performance penalty. Simulation results show that proposed algorithm performs very close to the algorithm of [1] in low SNR regime.
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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 | 0.001 |
| 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".