Pilot-guided Multimodal Semantic Communication for Audio-Visual Event Localization

TL;DR

This paper introduces a pilot-guided multimodal semantic communication framework for audio-visual event localization, improving robustness and performance over existing methods in dynamic real-world scenarios.

Contribution

It proposes a novel pilot-guided framework with Euler-based multimodal encoding and decoding, addressing the limitations of current single-modality and analog channel approaches.

Findings

Outperforms benchmarks in Signal-to-Noise Ratio (SNR)

Demonstrates robustness to channel variations

Supports diverse communication scenarios

Abstract

Multimodal semantic communication, which integrates various data modalities such as text, images, and audio, significantly enhances communication efficiency and reliability. It has broad application prospects in fields such as artificial intelligence, autonomous driving, and smart homes. However, current research primarily relies on analog channels and assumes constant channel states (perfect CSI), which is inadequate for addressing dynamic physical channels and noise in real-world scenarios. Existing methods often focus on single modality tasks and fail to handle multimodal stream data, such as video and audio, and their corresponding tasks. Furthermore, current semantic encoding and decoding modules mainly transmit single modality features, neglecting the need for multimodal semantic enhancement and recognition tasks. To address these challenges, this paper proposes a pilot-guided framework for multimodal semantic communication specifically tailored for audio-visual event localization tasks. This framework utilizes digital pilot codes and channel modules to guide the state of analog channels in real-wold scenarios and designs Euler-based multimodal semantic encoding and decoding that consider time-frequency characteristics based on dynamic channel state. This approach effectively handles multimodal stream source data, especially for audio-visual event localization tasks. Extensive numerical experiments demonstrate the robustness of the proposed framework in channel changes and its support for various communication scenarios. The experimental results show that the framework outperforms existing benchmark methods in terms of Signal-to-Noise Ratio (SNR), highlighting its advantage in semantic communication quality.