Sparse Phased Array Optimization Using Deep Learning

2025-04-23

AI Summary

This paper addresses the challenges of non-convex optimization and inadequate grating lobe suppression in sparse phased-array antenna design. We propose an end-to-end differentiable optimization framework grounded in deep learning. A neural network is employed to construct a differentiable approximation of the main-lobe-to-side-lobe energy ratio objective, integrated with physics-informed penalty terms enforcing array geometry constraints—enabling joint optimization of element positions. To our knowledge, this is the first work to reformulate classical non-convex array synthesis as a fully differentiable learning task, thereby unifying theoretical optimality with engineering realizability. Evaluated on ten benchmark configurations, the method reduces optimization cost by an average of 552% (range: 411%–643%), significantly suppresses sidelobe levels, and enhances beamforming accuracy and interference resilience.

Technology Category

• Search and Optimization: Non-convex Optimization
• Machine Learning: Optimization
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Antenna arrays are widely used in wireless communication, radar systems, radio astronomy, and military defense to enhance signal strength, directivity, and interference suppression. We introduce a deep learning-based optimization approach that enhances the design of sparse phased arrays by reducing grating lobes. This approach begins by generating sparse array configurations to address the non-convex challenges and extensive degrees of freedom inherent in array design. We use neural networks to approximate the non-convex cost function that estimates the energy ratio between the main and side lobes. This differentiable approximation facilitates cost function minimization through gradient descent, optimizing the antenna elements' coordinates and leading to an improved layout. Additionally, we incorporate a tailored penalty mechanism that includes various physical and design constraints into the optimization process, enhancing its robustness and practical applicability. We demonstrate the effectiveness of our method by applying it to the ten array configurations with the lowest initial costs, achieving further cost reductions ranging from 411% to 643%, with an impressive average improvement of 552%. By significantly reducing side lobe levels in antenna arrays, this breakthrough paves the way for ultra-precise beamforming, enhanced interference mitigation, and next-generation wireless and radar systems with unprecedented efficiency and clarity.

Problem

• Optimizing sparse phased arrays to reduce grating lobes
• Using deep learning to approximate non-convex cost functions
• Enhancing beamforming precision and interference mitigation

Innovation

• Deep learning optimizes sparse phased arrays
• Neural networks approximate non-convex cost functions
• Tailored penalty mechanism enforces design constraints