Contextual Wireless Video Semantic Communication in MIMO-OFDM Systems

TL;DR

This paper introduces M-CVST, a novel MIMO-OFDM-based framework for robust video semantic transmission that leverages channel correlation and recursive sampling to improve communication over multi-path channels.

Contribution

It proposes a new context-subcarrier correlation map and recursive sampling method to enhance semantic video transmission in MIMO-OFDM systems.

Findings

M-CVST outperforms existing schemes over multi-path MIMO channels.

The recursive subcarrier sampling improves channel state awareness.

Numerical results confirm the effectiveness of the proposed framework.

Abstract

This paper proposes a MIMO-OFDM-based context video semantic transmission framework, namely M-CVST, for robust video communication over multi-path multiple-input multiple-output (MIMO) channels. It introduces a context-subcarrier correlation map that aligns video feature context with groups of MIMO subcarriers. To leverage the time-correlated nature of multi-path channels, a recursive subcarrier sampling method paired with time-correlated reference embedding is designed, enabling the use of previously sampled MIMO subcarrier CSI to enhance channel state awareness in the entropy coding model. Numerical results verify the superiority of proposed M-CVST over MIMO multi-path channels compared to other semantic schemes and traditional separated schemes.