Fluid Antenna Index Modulation Communications

This work explores the integration of fluid antenna systems (FAS) and index modulation (IM) techniques to enhance the adaptability and performance of wireless communication systems. By leveraging software-controllable fluidic elements, fluid antennas enable the real-time adjustment of channel conditions. The introduction of IM allows for the efficient utilization of these dynamically adaptable antennas, exploiting independent channel variations for additional information delivery without occupying data transmission resources. Furthermore, the implementation of a wavelet scattering neural network (WSNN) in the proposed fluid antenna index modulation (FAIM) framework achieves fast classification of index patterns using limited training data. Results reveal that WSNN-assisted FAIM can accurately identify index patterns with better error performance than conventional orthogonal frequency division multiplexing (OFDM) signals, particularly in scenarios with high noise power. These findings suggest promising applications of FAIM in anti-jamming and resilient communication contexts.