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This paper proposes a cross-layer polar code based federated learning scheme to address communication bottlenecks and channel impairments, providing convergence analysis and resource optimization that demonstrates performance gains over uncoded and LDPC-based benchmarks.
This paper proposes TONIC, a token-centric semantic communication framework for task-oriented wireless systems that assigns utility-aware unequal error protection to tokens and uses confidence-aware gating with a Transformer-based completion model, outperforming baselines on image classification.