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

2025-01-28

AI Summary

In practical wireless MIMO-OFDM semantic communication systems, semantic distortion arises from power amplifier (PA) nonlinearity, high peak-to-average power ratio (PAPR), and frequency-selective fading—challenges unaddressed in prior semantic-aware physical-layer designs. Method: This work first systematically quantifies the coupled effect of PA nonlinearity and PAPR fluctuations on semantic link fidelity. It proposes a joint channel–hardware distortion compensation framework integrating deep semantic encoding/decoding, co-optimized MIMO-OFDM physical-layer design, post-distortion modeling for PA linearization, semantic-aware PAPR reduction, and channel equalization. Contribution/Results: Experimental evaluation on realistic hardware demonstrates an 8.2 dB improvement in semantic reconstruction PSNR under non-ideal channel conditions, with task-level accuracy approaching the Shannon limit. The results validate the feasibility of deploying semantic communication on 5G+/6G hardware platforms.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• MIMO-OFDM
• Signal Distortion
• Power Amplifier Instability

Innovation

• MIMO-OFDM Semantic Communication
• Nonlinear Power Amplifier Compensation
• Real-world Channel Characterization