Bridging Neural Networks and Wireless Systems with MIMO-OFDM Semantic Communications
This work addresses practical deployment challenges for semantic communication systems in wireless networks, though it appears incremental in nature.
This paper tackles the performance gap between theoretical simulations and real-world implementations of semantic communication systems in MIMO-OFDM wireless environments, identifying frequency selectivity as a key degradation factor and showing that targeted mitigation strategies can help these systems approach theoretical performance.
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.