Latent Space Alignment for AI-Native MIMO Semantic Communications

• URL: http://arxiv.org/abs/2507.16680v1
• Date: Tue, 22 Jul 2025 15:16:18 GMT
• Title: Latent Space Alignment for AI-Native MIMO Semantic Communications
• Authors: Mario Edoardo Pandolfo, Simone Fiorellino, Emilio Calvanese Strinati, Paolo Di Lorenzo,
• Abstract summary: We propose a novel approach to address semantic mismatches in semantic communications.<n>A neural network-based model learns semantic precoder/decoder under power budget and complexity constraints.<n>Results demonstrate the effectiveness of the proposed approach in a goal-oriented semantic communication scenario.
• Score: 11.185200213307208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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