Improved and weighted sum rate maximization for successive zero-forcing in multiuser MIMO systems
Bibliographic record
Abstract
We propose an improved algorithm for optimizing the transmit covariance matrices for successive zero-forcing (SZF) precoding in multiple-input multiple-output systems. We use a conjugate gradient projection method to solve the optimization problem. Our algorithm improves upon the existing method twofold. First, the existing covariance optimization method, with higher SNRs or more simultaneously supported users, can yield a lower sum rate than when using block diagonalization (BD), when theoretically SZF should never be inferior to BD. In comparison, our method consistently provides a higher throughput than BD. Several simulations demonstrate our algorithm's enhanced performance, which exceeds the existing method's sum rate by up to 12% in the examined cases. Second, our proposed algorithm also supports the maximization of a weighted sum rate with SZF, to incorporate quality of service (QoS). To our knowledge, no other work in the literature considers a weighted sum rate and/or QoS with SZF.
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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".