标题： 通过MIMO-OFDM语义通信连接神经网络和无线系统 显示英文标题

标题： Bridging Neural Networks and Wireless Systems with MIMO-OFDM Semantic Communications

摘要： 语义通信旨在通过联合优化源编码、信道编码和调制来提高传输效率。 尽管先前的研究在仿真中展示了有前景的性能，但实际应用中常常面临重大挑战，包括噪声变化和非线性失真，导致性能差距。 本文研究了基于多输入多输出（MIMO）和正交频分复用（OFDM）的语义通信系统中的这些挑战，重点关注功率放大器（PA）非线性和峰均功率比（PAPR）变化的实际影响。 我们的分析识别出实际信道的频率选择性是性能退化的关键因素，并表明有针对性的缓解策略可以使语义系统接近理论性能。 通过解决现有设计中的关键限制，我们为在实际无线环境中推进语义通信提供了可行的见解。 这项工作为弥合理论模型与实际部署之间的差距奠定了基础，突出了系统设计和优化中的关键考虑因素。 显示英文摘要

摘要： Semantic communications aim to enhance transmission efficiency by jointly optimizing source coding, channel coding, and modulation. While prior research has demonstrated promising performance in simulations, real-world implementations often face significant challenges, including noise variability and nonlinear distortions, leading to performance gaps. This article investigates these challenges in a multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM)-based semantic communication system, focusing on the practical impacts of power amplifier (PA) nonlinearity and peak-to-average power ratio (PAPR) variations. Our analysis identifies frequency selectivity of the actual channel as a critical factor in performance degradation and demonstrates that targeted mitigation strategies can enable semantic systems to approach theoretical performance. By addressing key limitations in existing designs, we provide actionable insights for advancing semantic communications in practical wireless environments. This work establishes a foundation for bridging the gap between theoretical models and real-world deployment, highlighting essential considerations for system design and optimization.