Neuro-Symbolic AI for Self-Evolving Signal Processing in Autonomous Communication Systems
Raj Kashikar · 2024
The rapid advancement of autonomous systems demands highly adaptive and secure communication protocols. To address these challenges, we present a novel neuro-symbolic AI framework that enables real-time self-evolving signal processing in highly dynamic and security-sensitive autonomous communication systems. By integrating the strengths of neural networks in pattern recognition with the logical, adaptive decision-making capabilities of symbolic AI, the system autonomously optimizes communication protocols without human intervention. This approach leverages multi-scale convolutional neural networks (CNNs) for hierarchical signal feature extraction and uses symbolic AI for rule-based adaptation. Additionally, quantum key distribution (QKD) is employed to secure the evolving communication channels. Through comprehensive simulations, this system achieved a substantial improvement in signal-tonoise ratio (SNR) by $33 \% \pm 2 \%$ and reduced bit error rate (BER) by $44 \% \pm 3 \%$, outperforming existing models. Furthermore, the proposed framework’s ability to dynamically adapt to new environmental stimuli and secure communications in real time introduces a groundbreaking self-evolving communication system.