Near-Field Nulling Control Beamfocusing Optimization for Multi-User Interference Suppression
Bibliographic record
Abstract
This paper presents comprehensive full-wave simulation-based studies of near-field beam radiation patterns for large-scale arrays, accounting for realistic electromagnetic wave characteristics, heterogeneous element radiation patterns, and array element interactions. These simulations thoroughly investigate and illustrate the radiation behaviors of antenna arrays at different observation distances. To leverage the advantages offered by distance-dependent radiation patterns in the near-field, we consider two nulling control beamfocusing algorithms to effectively mitigate multi-user interference (MUI) in massive multiple-input multiple-output (mMIMO) systems by achieving considerable focusing gain differences between the desired and interference locations. Firstly, a linear constraint minimum variance (LCMV) scheme to effectively control radiation nulls in the Fresnel region is developed. By adjusting the array feeding magnitudes and phase shifters, an average gain difference of 29.2 dB between desired and undesired users can be achieved, with minimal gain degradation of 0.4 dB at the desired user compared to the maximum directivity beamfocusing scheme. Moreover, a constant-modulus beamfocusing scheme based on a perturbation-based nulling control beamfocusing algorithm employing particle swarm optimization is proposed. Using only phase shifters, an average gain difference of 26.1 dB between desired and undesired users can be achieved. Iterative full-wave simulations are conducted to investigate how the achievable beamfocusing gain difference varies with different desired and interference user locations. Finally, a deep neural network (DNN) is trained for MUI suppression based on the LCMV-generated beamfocusing vectors. The model achieves a phase error of less than 0.021 radians and a magnitude error of 0.17 dB in the predicted feeding weights. The resulting near-field beam patterns using the LCMV-based vector and the DNN-predicted vector show good agreement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".