Performance Evaluation of MU-MIMO Systems with Multi-Antenna Users for Different Precoding Strategies
Bibliographic record
Abstract
Radio resource management of the downlink of multi-user multiple-input multiple-output systems with multi-antenna users is considered to evaluate the performance of three zero-forcing precoding strategies: 1) Block Diagonalization (BD) where all streams are used for each selected user; 2) Coordinated-Transmit-Receive-1$(\mathbf{CTR}_{\mathbf{1}})$where only the strongest stream is used for each scheduled user; 3) Coordinated-Transmit-Receive-Flexible$(\mathbf{CTR}_{\mathbf{F}})$that allows a flexible stream allocation per selected user. Although the more complex$\mathbf{CTR}_{\mathbf{F}}$has the potential for better performance due to its flexibility, it might be difficult to implement it in practice. Hence, it is crucial to comprehend when the simpler CTR1or BD can be used instead of CT$\mathbf{R}_{\mathbf{F}}$. Our analysis compares the precoding strategies within$\mathbf{3GPP}$-compliant scenarios using realistic modulation and coding schemes in systems featuring large numbers of users and Base-Station (BS) antennas. We compare the performance of these precoding strategies under Sum-Rate (SR) maximization and Proportional Fairness (PF) and show that previous research conclusions carried out for SR maximization only hold for a small number of BS antennas. Indeed, for SR maximization, BD matches the performance of$\mathbf{CTR}_{\mathbf{F}}$when BS antenna arrays are sufficiently large. For PF, the results are more nuanced, depending significantly on system parameters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".