Hybrid AI and Blockchain-Enhanced Signal Processing for Next-Generation 6G Channel Estimation

Abstract

The rapid development pace of 6G network necessitates deployment of precise, dependable and adaptable channel estimation frameworks that have the ability to perform under extremely dynamic propagation circumstances. Although the traditional deep-learning-based estimators can be highly accurate, not very transparent, and the consistency of the data is low, they cannot be applied to a decentralized architecture. To address these limitations, this study proposes a channel estimation framework named Hybrid AI and Blockchain-Enhanced (HABCE). The CNN-LSTM based estimator along with an effective blockchain based consensus system is a collaborative approach, known as the HABCE architecture. The system has been Proof-of-Learning (PoL) validated, so that local system model update is strongly validated, and globally aggregated to create independent and high integrity estimation ecosystem. HABCE has been found to attain an improved NMSE of up to 4-5 dB (p<0.05) and 30% latency and 25% energy savings as compared to baseline through Performance Experimental testing with 6G Terahertz digital-twin data. Results show that the HABCE framework might be a practical path towards becoming trustful, energy-efficient and verifiable AI-assisted signal processing to provide the cornerstone of scalable, transparent and adaptive channel estimation in future 6G communication systems.