解决无线语义通信在真实环境中的性能下降问题。
Bridging Neural Networks and Wireless Systems with MIMO-OFDM Semantic Communications
- 结合MIMO-OFDM系统，分析功率放大器非线性与峰均比影响。
- 发现实际信道频率选择性是导致性能下降的关键因素。
- 提出针对性优化策略，推动理论到现实的落地应用。
语义通信旨在通过联合优化源编码、信道编码和调制来提升传输效率。尽管先前研究在仿真中表现出色，但真实无线环境中噪声变化和非线性失真常导致性能差距。本文针对基于多输入多输出（MIMO）与正交频分复用（OFDM）的语义通信系统，分析功率放大器（PA）非线性和峰值平均功率比（PAPR）变化的实际影响。研究发现，实际信道的频率选择性是性能退化的主要因素，并表明针对性缓解措施可使系统逼近理论性能。通过克服现有设计的关键局限，本工作为语义通信在实际无线环境中的推进提供了可操作洞见，建立了理论模型与实际部署之间的桥梁，强调了系统设计与优化中的关键考量。
原文摘要 · Abstract (English)
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.
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