Compact Attention-Augmented Neural Decoder for Chaos-Based Wireless Communications
Marco Siino, Stefano Mangione, Ilenia Tinnirello · IEEE Communications Letters · 2026
Chaos-based modulation offers strong interference resilience but decoding remains challenging under low SNRs and fading channels. Existing deep learning receivers achieve promising results but are often too large for resource-constrained systems. We propose Ultra-CAN, an ultra-light convolutional–attention decoder for Chaos Shift Keying (CSK) that combines efficient convolutional feature extraction with a compact attention block to capture long-range dependencies. Our design achieves superior Bit Error Rate (BER) performance compared to state-of-the-art demodulators in AWGN and Rayleigh channels, with a footprint of 220.4 kB. Results demonstrate the viability of compact attention-based decoders for chaotic communications.