Dottorato in DATA SCIENCE

Titolo della tesi: Latent Space Alignment in AI-Native Communications: Theory & Applications

This thesis studies communication between independently trained artificial intelligence models from a latent-space perspective. We move beyond bit-centric abstractions and view AI-native communi- cation systems as exchanging structured representations, where failures arise from geometric incompatibility between transmitter and receiver latent spaces. Accordingly, semantic noise is modeled as latent-space mismatch, that is, structural misalignment between heterogeneous representation spaces that prevents direct semantic interpretability. The thesis develops a unified framework for Semantic Channel Equalization (SCEq) in AI-native communications. Building on the concept of Relative Representations (RRs), we propose their use as a semantic equalization mechanism enabling zero-shot alignment between independently trained encoders, without joint retraining or model sharing. We then introduce a frame-based reformulation leading to the Parseval Frame Equalizer (PFE), yielding numerically stable reconstruction operators, controlled semantic compression, and robustness to perturbations via well-conditioned linear analysis–synthesis mappings. Moving beyond the zero-shot, semantic-only setting, we next consider the coupling of latent space alignment with wireless transmission, formulating a joint physical–semantic equalization prob- lem over Multiple-Input and Multiple-Output (MIMO) channels. Semantic precoding and decoding are optimized to jointly compensate channel impairments and latent-space misalignment. We study both linear and neural formulations, leading to tractable alternating optimization in the linear case and higher-capacity nonlinear alignment in the neural case. The approach is further inte- grated into Deep Joint Source–Channel Coding (DeepJSCC) architectures, where heterogeneous encoder–decoder pairs require additional alignment stages to preserve semantic consistency. Finally, we address dynamic, resource-constrained semantic communication scenarios via a stochastic optimization framework that jointly adapts communication, computation, and learning parameters, while preserving semantic alignment under latency and accuracy constraints. Overall, the thesis provides a mathematically grounded, system-level treatment of latent space alignment in AI-native communications, establishing semantic channel equalization as a central component for interoperability across heterogeneous intelligent agents.