FPGA-Accelerated AI for Demultiplexing Multimode Fiber Toward Next-Generation Communications

With the exponential growth of global data traffic driven by AI, big-data analytics, and cloud computing, today’s single-mode fiber (SMF) networks are edging toward their Shannon-capacity limits. Space-division multiplexing (SDM) in multimode fiber (MMF) has emerged as a leading candidate for the next-generation bandwidth breakthrough because a single MMF can carry many orthogonal transverse modes in parallel. However, random mode coupling during propagation mixes these modes into complex speckle patterns, severely complicating signal recovery. Although conventional digital signal processing (DSP) algorithms are theoretically capable of mode demultiplexing, their computational complexity scales rapidly with the number of modes, rendering them impractical for high-capacity MMF networks.