Neural Network-Assisted RIS Weight Optimization for Spatial Nulling in Distorted Reflector Antenna Systems
Abstract
Reconfigurable intelligent surfaces (RIS) have recently been proposed as an effective means for spatial interference suppression in large reflector antenna systems. Existing RIS weight optimization algorithms typically rely on accurate theoretical radiation models. However, in practice, distortions on the reflector antenna may cause mismatches between the theoretical and true antenna patterns, leading to degraded interference cancellation performance when these weights are directly applied. In this report, a residual learning network-assisted simulated annealing (ResNet-SA) framework is proposed to address this mismatch without requiring explicit knowledge of the distorted electric field. By learning the residual difference between the theoretical and true antenna gains, a neural network (NN) is embedded in a heuristic optimization algorithm to find the optimal weight vector. Simulation results demonstrate that the proposed approach achieves improved null depth in the true radiation pattern as compared with conventional methods that optimize weights based solely on the theoretical model, validating the effectiveness of the ResNet-SA algorithm for reflector antenna systems with approximate knowledge of the pattern.
Summary
This paper addresses the problem of spatial interference suppression in large reflector antenna systems using reconfigurable intelligent surfaces (RIS). Existing RIS weight optimization algorithms rely on accurate theoretical radiation models, but distortions in the reflector antenna can cause mismatches between the theoretical and true antenna patterns, leading to degraded interference cancellation. The paper proposes a residual learning network-assisted simulated annealing (ResNet-SA) framework to address this mismatch without requiring explicit knowledge of the distorted electric field. The core idea is to train a neural network (NN) to learn the residual difference between the theoretical and true antenna gains. This NN is then embedded within a simulated annealing (SA) optimization algorithm to find the optimal RIS weight vector. The ResNet-SA algorithm first trains a residual learning network using measured data of the antenna's performance. The training data consists of pairs of theoretical and measured gains corresponding to different RIS weight configurations. Once trained, the ResNet predicts the gain mismatch for a given weight vector and theoretical gain. The simulated annealing algorithm then uses this predicted gain to determine whether to accept a candidate weight vector, effectively optimizing the weights based on the NN's estimate of the true antenna performance. Simulation results demonstrate that the ResNet-SA approach achieves improved null depth in the true radiation pattern compared to conventional methods that rely solely on the theoretical model. This is significant as it provides a practical solution for improving interference suppression in real-world reflector antenna systems where distortions are unavoidable.
Key Insights
- •The paper introduces a novel ResNet-SA framework that combines a residual learning network with a simulated annealing algorithm for RIS weight optimization in distorted reflector antenna systems.
- •The key idea is to learn the difference between the theoretical and true antenna gains using a neural network, eliminating the need for explicit knowledge of the reflector's distortion.
- •Simulation results show that the ResNet-SA algorithm achieves a deeper null depth in the true radiation pattern compared to conventional methods that only use the theoretical model (see Figure 4, 5, 6).
- •The performance of the ResNet-SA is dependent on the size and diversity of the training dataset used to train the neural network. Increasing the training dataset from 4000 to 80000 samples improves the accuracy of the gain prediction and enhances the null depth achieved by the ResNet-SA algorithm.
- •The paper uses a parametric perturbation of the feed model (varying the 'q' parameter) to simulate antenna distortions, providing a convenient and repeatable method for evaluating the proposed algorithm.
- •The algorithm is designed to work with discrete phase shifts for the RIS elements, making it suitable for practical implementations with hardware limitations.
- •The paper builds upon the authors' previous work on simulated annealing and extreme point pursuit methods for RIS weight optimization, extending these techniques to handle antenna distortions.
Practical Implications
- •The ResNet-SA algorithm can be used to improve the performance of large reflector antennas in applications such as radio astronomy and satellite communication, where spatial interference suppression is crucial.
- •Engineers can use this algorithm to optimize the RIS weights in existing reflector antenna systems without needing detailed knowledge of the antenna's distortion profile.
- •The algorithm can be implemented offline, as the NN training and weight optimization can be performed before deployment.
- •Future research can explore the use of more sophisticated distortion models and the development of online learning techniques to adapt the NN to time-varying distortions.
- •This research opens up avenues for developing more robust and adaptive interference mitigation techniques for a wide range of antenna systems.