Latent Space Alignment for AI-Native MIMO Semantic Communications

TL;DR

This paper proposes a novel MIMO-based semantic communication method that aligns latent spaces to reduce semantic mismatches and improve task-oriented data transmission, using both linear and neural network models.

Contribution

It introduces a joint latent space alignment approach for semantic MIMO communications, addressing semantic mismatches and channel impairments with new optimization solutions.

Findings

The neural network model effectively learns semantic MIMO precoders/decoders.

The linear model optimized via ADMM achieves competitive performance.

Trade-offs between accuracy, complexity, and communication load are demonstrated.

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.