Antenna Coding Optimization for Pixel Antenna Empowered Wireless Communication Using Deep Learning with Heterogeneous Multi-Head Selection
Binzhou Zuo, Shanpu Shen, Hongyu Li

TL;DR
This paper introduces a deep learning-based antenna coding optimization method for pixel antennas, significantly reducing computational complexity while maintaining high system performance in wireless communication systems.
Contribution
It presents a novel heterogeneous multi-head selection deep learning algorithm that outperforms traditional heuristic methods in efficiency and effectiveness.
Findings
Achieves 98% of searching-based algorithm performance
81 times faster in SISO systems
297 times faster in MIMO systems
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…
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Taxonomy
TopicsWireless Signal Modulation Classification · Advanced Data Compression Techniques · Advanced MIMO Systems Optimization
