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

TL;DR

This paper explores the practical challenges of implementing MIMO-OFDM semantic communications, focusing on nonlinear distortions and channel effects, and proposes mitigation strategies to improve real-world performance.

Contribution

It identifies key factors like PA nonlinearity and PAPR variations affecting performance and offers solutions to bridge the gap between theory and practice in wireless semantic systems.

Findings

Frequency selectivity significantly impacts performance.

Targeted mitigation strategies improve real-world system performance.

Bridging the gap between theoretical models and practical deployment.

Abstract

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.