Antenna Position Optimization of Sparse Arrays for Near-Field Multiuser Communications
Bibliographic record
Abstract
The near-field communications have shown various improvement over the far-field ones benefiting from the unique near-field effects. However, most of the existing works exploit the benefits of near-field communications by employing a large number of antennas, which entails exorbitant hardware costs. In this paper, we consider multiuser communications based on sparse arrays (SAs) to exploit the near-field effects for sum-rate improvement with low hardware costs. To maximize the ergodic sum-rate of near-field multiuser communications, we optimize the antenna positions of SAs under the limitations of antenna panel size and antenna spacings. Using the maximum ratio combining, the maximization of the ergodic sum-rate is formulated as the minimization of the correlations among channel steering vectors. Since the problem of channel steering vector correlation minimization is nonconvex, an effective successive convex approximation-based antenna position optimization algorithm is proposed. Simulation results show that the proposed method can significantly improve the sum-rate over the existing methods with the same hardware costs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".