RIS-aided Latent Space Alignment for Semantic Channel Equalization

2025-07-22

AI Summary

In multi-user semantic communication, independently trained interactive agents suffer from latent-space semantic mismatch due to the absence of joint optimization and shared contextual knowledge, severely impairing cross-device semantic understanding. To address this, we propose a novel channel equalization framework jointly operating at the physical and semantic layers—introducing reconfigurable intelligent surfaces (RIS) into semantic communication for the first time. Our framework implements cascaded transformations comprising transmitter-side pre-equalization, RIS-assisted propagation control, and receiver-side post-equalization to achieve cross-device latent-space alignment over MIMO channels. It synergistically integrates linear (MMSE) and nonlinear (DNN-based) equalization mechanisms, eliminating the need for joint agent training while effectively mitigating semantic inconsistency. Experimental results demonstrate that the proposed scheme significantly improves semantic transmission efficiency and reliability across diverse channel conditions, outperforming conventional decoupled equalization approaches.

Technology Category

• Multiagent Systems: Agent Communication
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Semantic communication systems introduce a new paradigm in wireless communications, focusing on transmitting the intended meaning rather than ensuring strict bit-level accuracy. These systems often rely on Deep Neural Networks (DNNs) to learn and encode meaning directly from data, enabling more efficient communication. However, in multi-user settings where interacting agents are trained independently-without shared context or joint optimization-divergent latent representations across AI-native devices can lead to semantic mismatches, impeding mutual understanding even in the absence of traditional transmission errors. In this work, we address semantic mismatch in Multiple-Input Multiple-Output (MIMO) channels by proposing a joint physical and semantic channel equalization framework that leverages the presence of Reconfigurable Intelligent Surfaces (RIS). The semantic equalization is implemented as a sequence of transformations: (i) a pre-equalization stage at the transmitter; (ii) propagation through the RIS-aided channel; and (iii) a post-equalization stage at the receiver. We formulate the problem as a constrained Minimum Mean Squared Error (MMSE) optimization and propose two solutions: (i) a linear semantic equalization chain, and (ii) a non-linear DNN-based semantic equalizer. Both methods are designed to operate under semantic compression in the latent space and adhere to transmit power constraints. Through extensive evaluations, we show that the proposed joint equalization strategies consistently outperform conventional, disjoint approaches to physical and semantic channel equalization across a broad range of scenarios and wireless channel conditions.

Problem

• Address semantic mismatch in MIMO channels using RIS-aided equalization
• Align divergent latent representations in multi-user semantic communication systems
• Optimize joint physical and semantic channel equalization under power constraints

Innovation

• RIS-aided joint physical-semantic channel equalization
• Linear and non-linear DNN-based semantic equalizers
• Pre- and post-equalization stages with RIS optimization