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Cavity Duplexer Tuning with 1d Resnet-like Neural Networks

Anton Raskovalov

TL;DR

The paper addresses automated tuning of complex cavity duplexers with many screws by reframing the problem as supervised learning using a 1d-ResNet-like actor that consumes S-parameter curves. It replaces reinforcement learning with two supervised strategies, ultimately relying on a ResNet backbone combined with peak encoders and a forcing mechanism to produce per-screw actions, guided by a solver that performs multi-step refinements. Key contributions include architecture designs for 1d convolutional processing of S-parameters, peak-encoder variants, and a robust solver with a fine-tuning stage, achieving near-tuned states with only 4–5 screw rotations per screw. The approach demonstrates practical potential for fast, repeatable duplexer tuning in RF hardware, with improvements in generalization when incorporating curve-shape metrics and peak information.

Abstract

This paper presents machine learning method for tuning of cavity duplexer with a large amount of adjustment screws. After testing we declined conventional reinforcement learning approach and reformulated our task in the supervised learning setup. The suggested neural network architecture includes 1d ResNet-like backbone and processing of some additional information about S-parameters, like the shape of curve and peaks positions and amplitudes. This neural network with external control algorithm is capable to reach almost the tuned state of the duplexer within 4-5 rotations per screw.

Cavity Duplexer Tuning with 1d Resnet-like Neural Networks

TL;DR

The paper addresses automated tuning of complex cavity duplexers with many screws by reframing the problem as supervised learning using a 1d-ResNet-like actor that consumes S-parameter curves. It replaces reinforcement learning with two supervised strategies, ultimately relying on a ResNet backbone combined with peak encoders and a forcing mechanism to produce per-screw actions, guided by a solver that performs multi-step refinements. Key contributions include architecture designs for 1d convolutional processing of S-parameters, peak-encoder variants, and a robust solver with a fine-tuning stage, achieving near-tuned states with only 4–5 screw rotations per screw. The approach demonstrates practical potential for fast, repeatable duplexer tuning in RF hardware, with improvements in generalization when incorporating curve-shape metrics and peak information.

Abstract

This paper presents machine learning method for tuning of cavity duplexer with a large amount of adjustment screws. After testing we declined conventional reinforcement learning approach and reformulated our task in the supervised learning setup. The suggested neural network architecture includes 1d ResNet-like backbone and processing of some additional information about S-parameters, like the shape of curve and peaks positions and amplitudes. This neural network with external control algorithm is capable to reach almost the tuned state of the duplexer within 4-5 rotations per screw.
Paper Structure (12 sections, 2 equations, 7 figures, 1 table)

This paper contains 12 sections, 2 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Terms and Metrics
  4. From RL to supervised learning
  5. Updater -- Actor scheme
  6. Actor trained with "true" actions
  7. Architectures
  8. Peak Encoders
  9. Solver
  10. Results
  11. Generalization
  12. Conclusion

Figures (7)

  • Figure 1: An example of S11 curve, two passbands are bounded by vertical dash lines. The discussed metrics, areas (formula \ref{['eq:1']}), are colored pink in both passbands. For the tuned cavity filter these areas must be equal to zero.
  • Figure 2: The impossibility of training using a naive scheme (a) and ways to circumvent this limitation (b, c).
  • Figure 3: Elements of ResNet architecture: (a) a ResNet block which has 4 initialization parameters (the number of input channels, the number of bottleneck channels, the stride value and arbitrary neural network downsample (it may be empty)); (b) a ResNet layer with 4 parameters (the number of input channels, the number of bottleneck channels; the stride value and the number of blocks), note that the stride parameter applied only to the first block in the layer, other blocks have stride = 1, to fit tensor dimensionalities; (c) our variant of ResNet layer with some elements replaced with maxpooling operation.
  • Figure 4: Peak encoders: (a) simple -- just pass (x, y) of peaks into a feed forward neural network; (b) (x, y) pass into feed forward and (x) pass into another feed forward net and the result of subsequent softmax is used as mask for peak embedding. Both variants contain subsequent aggregation by S-parameter index. In the case (b) logits for softmax can be split on several parts ("heads").
  • Figure 5: The solver algorithm with several full steps and a fine-tuning stage.
  • ...and 2 more figures