  Radio Frequency Neural Networks (RFNNs) have demonstrated advantages in
realizing intelligent applications across various domains. However, as the
model size of deep neural networks rapidly increases, implementing large-scale
RFNN in practice requires an extensive number of RF interferometers and
consumes a substantial amount of energy. To address this challenge, we propose
to utilize low-rank decomposition to transform a large-scale RFNN into a
compact RFNN while almost preserving its accuracy. Specifically, we develop a
Tensor-Train RFNN (TT-RFNN) where each layer comprises a sequence of low-rank
third-order tensors, leading to a notable reduction in parameter count, thereby
optimizing RF interferometer utilization in comparison to the original
large-scale RFNN. Additionally, considering the inherent physical errors when
mapping TT-RFNN to RF device parameters in real-world deployment, from a
general perspective, we construct the Robust TT-RFNN (RTT-RFNN) by
incorporating a robustness solver on TT-RFNN to enhance its robustness. To
adapt the RTT-RFNN to varying requirements of reshaping operations, we further
provide a reconfigurable reshaping solution employing RF switch matrices.
Empirical evaluations conducted on MNIST and CIFAR-10 datasets show the
effectiveness of our proposed method.
