Improving Beamforming Performance With Practical Phase Shifters Using Robust Mapping and Deep Learning
Bibliographic record
Abstract
In this article, the problem of beamforming and maximizing the phased array gain for practical phase shifters is addressed using a deep learning approach. For an ideal phase shifter, the amplitude is constant and the output phase changes linearly with the control voltage. However, the practical phase shifters show a nonlinear behavior in both amplitude and phase with respect to the control voltage. The main objective of this article is to design a deep neural network (DNN), which estimates the optimum voltage values of the phase shifter using the noisy data of the receiver antenna array. The proposed approach is general and extendible to any array geometry. Usually, the quality of the trained model is affected by large variations in the voltage values for certain adjacent directions of arrival (DOAs). To overcome this issue, we introduce the robust mapping technique, which avoids sudden changes in voltage values during the training process. The amplitude and phase data of a typical reflective-type phase shifter is measured for different control voltage values, to be used in the training process. The suggested algorithm is applied to a receiver array of four antennas with reflective-type phase shifters (RTPSs). The maximum array gain for different DOAs is very close to the optimum results showing the superior performance of the proposed method. Compared to the existing methods, the root mean square error (RMSE) of the array gain decreased to 0.0283. It is shown that the proposed method is more resilient to noise and performs substantially better than other methods in low SNR scenarios.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".