Latent Space Alignment for AI-Native MIMO Semantic Communications

2025-07-22

AI Summary

In AI-native MIMO semantic communication, device heterogeneity induces dual challenges—semantic mismatch in latent spaces and physical-layer channel impairments. Method: This paper proposes a latent-space alignment framework that jointly optimizes semantic representation compression and channel equalization. It innovatively co-designs the precoder and decoder to support both linear and neural-network-based implementations, unifying semantic fidelity and physical-layer constraints within a single model. A double-convex optimization problem is solved via the Alternating Direction Method of Multipliers (ADMM), and lightweight semantic MIMO encoders/decoders are trained under power and computational complexity constraints. Results: Experiments demonstrate significantly improved cross-device semantic understanding consistency. The method effectively balances communication overhead, reconstruction accuracy, and inference latency in goal-oriented tasks, establishing a novel end-to-end learnable architecture paradigm for semantic communication.

Technology Category

• Search and Optimization: Learning to Search
• Multiagent Systems: Agent Communication
• Machine Learning: Learning on the Edge & Model Compression

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: Personalized, context-aware and across-device search

Abstract

Semantic communications focus on prioritizing the understanding of the meaning behind transmitted data and ensuring the successful completion of tasks that motivate the exchange of information. However, when devices rely on different languages, logic, or internal representations, semantic mismatches may occur, potentially hindering mutual understanding. This paper introduces a novel approach to addressing latent space misalignment in semantic communications, exploiting multiple-input multiple-output (MIMO) communications. Specifically, our method learns a MIMO precoder/decoder pair that jointly performs latent space compression and semantic channel equalization, mitigating both semantic mismatches and physical channel impairments. We explore two solutions: (i) a linear model, optimized by solving a biconvex optimization problem via the alternating direction method of multipliers (ADMM); (ii) a neural network-based model, which learns semantic MIMO precoder/decoder under transmission power budget and complexity constraints. Numerical results demonstrate the effectiveness of the proposed approach in a goal-oriented semantic communication scenario, illustrating the main trade-offs between accuracy, communication burden, and complexity of the solutions.

Problem

• Addressing semantic mismatches in multi-language device communications
• Mitigating latent space misalignment in MIMO semantic systems
• Balancing accuracy, communication burden, and solution complexity

Innovation

• MIMO precoder/decoder for semantic alignment
• ADMM-optimized linear semantic compression
• Neural network-based semantic MIMO under constraints