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.

翻译：像素天线是一种前景广阔的无线技术，可通过天线编码灵活调整辐射特性并增强无线系统性能。本文提出了一种基于深度学习的新型天线编码优化算法。该算法由异构多头选择机制支撑，其核心思想是基于多种编码方案训练多个神经网络，并选择能带来最佳系统性能的网络。与传统基于启发式搜索的算法需要高计算复杂度才能获得满意性能不同，所提出的数据驱动深度学习方法能以显著降低的计算复杂度达到搜索算法98%的性能水平。结果表明，在像素天线赋能的单输入单输出系统中，所提算法的计算速度比搜索算法快81倍；在更复杂的像素天线赋能多输入多输出系统中，计算速度比现有搜索算法快297倍。凭借高性能与低计算复杂度的优势，该算法证明了像素天线作为增强无线系统性能的新型实用技术具有巨大潜力。