Clustered Latent Space Alignment for Multi-User Semantic Communications

Semantic Communication has emerged as a promising paradigm to overcome the limitations of conventional bitlevel transmission by focusing on the exchange of task-relevant information rather than raw data. However, when heterogeneous devices rely on independently trained neural encoders and decoders, latent space mismatches arise, generating semantic noise and degrading downstream task performance. This issue becomes particularly critical in multi-user scenarios, where efficiency and reduced computational complexity must be jointly addressed. A cluster-based latent space alignment framework for multi-user semantic communications is introduced. Users are grouped according to a similarity measure reflecting their communication requirements, and clusters are identified through a graph-based procedure that captures structural affinities among users. A shared semantic equalizer is then learned for each cluster, enabling efficient alignment between the access point and users while enforcing per-cluster power constraints. The resulting optimization problem is addressed through an alternating optimization strategy, iteratively updating cluster-level and userspecific equalizers. Simulation results on an image classification task with heterogeneous pre-trained models demonstrate that the proposed approach achieves high task accuracy compared with benchmarks, confirming the framework's effectiveness in balancing semantic alignment accuracy and cost efficiency in multi-user settings.