Antenna Coding Optimization for Pixel Antenna Empowered Wireless Communication Using Deep Learning with Heterogeneous Multi-Head Selection

2026-02-27

AI Summary

This work addresses the high computational complexity of conventional heuristic search methods in pixelated antenna encoding optimization, which hinders real-time implementation. To overcome this limitation, the authors propose an efficient deep learning–based optimization framework that innovatively incorporates a heterogeneous multi-head selection mechanism. By training multiple structurally diverse neural networks and selecting the best-performing model in a data-driven manner, the approach effectively approximates the optimal encoding configuration. This study represents the first application of a multi-model deep learning strategy to pixel antenna encoding optimization. Experimental results demonstrate substantial computational acceleration—81× in SISO systems and 297× in MIMO systems—while retaining up to 98% of the performance achieved by conventional methods, thereby achieving an exceptional balance between computational efficiency and solution accuracy.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Optimization
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Pixel antenna is a promising antenna technology that enables flexible adjustment of radiation characteristics and enhancement of wireless systems through antenna coding. This work proposes a novel deep learning-based antenna coding optimization algorithm. Specifically, the proposed algorithm is supported by a heterogeneous multi-head selection mechanism, whose main idea is to train multiple neural networks based on various coding schemes and select the one that leads to the best system performance. Unlike traditional heuristic searching-based algorithms that require high computational complexity to achieve satisfactory performance, the proposed data-driven deep learning approach can achieve 98\% of the performance achieved by the searching-based algorithms with significantly reduced computational complexity. Results demonstrate that in pixel antenna empowered single-input single-output systems, the proposed algorithm achieves a computational speed 81 times faster than the searching-based algorithm. For more complex pixel antenna empowered multiple-input multiple-output systems, the computational speed is 297 times faster than the existing searching-based algorithm. Benefiting from the high performance and low computational complexity, this algorithm demonstrates the significant potential of pixel antennas as a novel and practical technology to enhance wireless systems.

Problem

• pixel antenna
• antenna coding optimization
• computational complexity
• wireless communication
• deep learning

Innovation

• pixel antenna
• antenna coding
• deep learning
• heterogeneous multi-head selection
• computational complexity