Multi-Cell Semantic Interference Management

Derive and validate a multi-cell semantic-interference model and resource-allocation framework that accounts for correlations between semantic feature spaces and knowledge bases, in addition to conventional physical inter-cell interference, in dense TinyLM-enabled 6G deployments.

Background

The paper argues that conventional interference management treats interference as a physical channel phenomenon, whereas semantically similar transmissions can also interfere at the representation and receiver-inference levels. Receivers may conflate features from different transmitters when their knowledge bases or concept spaces overlap.

An extended received-feature equation is proposed with a semantic-interference term, but the paper identifies the corresponding modeling and allocation problem as unresolved, particularly for dense multi-cell deployments.