Channel Transmission Model Based on IFDM and Transformer

This paper proposes a channel transmission model based on Interleaved Frequency Division Multiplexing (IFDM) and a Transformer architecture, aiming to address the performance bottlenecks of traditional Orthogonal Frequency Division Multiplexing (OFDM) systems in dynamic channel adaptation and complex channel decoding. This Transformer model abandons the traditional encoder-decoder structure and directly models the global dependencies between symbols using a multi-head attention mechanism, enabling intelligent channel decoding. This architecture provides a new solution for intelligent communication systems in complex channel environments, achieving performance close to the maximum a posteriori probability (MAP) decoding while significantly reducing computational resource consumption.