An Adaptive Approach for the Joint Antenna Selection and Beamforming Optimization
Bibliographic record
Abstract
Adaptive beamforming techniques are widely known for their capability of leveraging the performance of antenna arrays. The effectiveness of such techniques typically increases as the number of antennas grows. In contrast, computational and hardware costs very often limit the deployment of beamforming in large-scale arrays. To circumvent this problem, antenna selection strategies have been developed aiming to maintain much of the performance gain obtained by using a large array while keeping computational and hardware costs at acceptable levels. In this context, the present paper is dedicated to the development of two new adaptive algorithms for solving the problem of joint antenna selection and beamforming for uplink reception in mobile communication systems. Both algorithms are based on an alternating optimization strategy and are designed to operate with a limited number of radio-frequency chains. The main difference between the proposed algorithms is that the first is formulated by considering the minimum mean-square error (MMSE) criterion, while the second is based on the minimum-variance distortionless-response (MVDR) approach. The numerical simulation results confirm the effectiveness of the proposed algorithms.
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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 | 0.002 |
| 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) | 0.002 | 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".