Wireless-Powered Cooperative Key Generation for e-Health: A Reservoir Learning Approach
Mehdi Letafati, Hamid Behroozi, Babak Hossein Khalaj, Eduard A. Jorswieck
Abstract
Digital healthcare services are rapidly evolving for new methodologies, including hospital-to-home (H2H) services and intelligent Internet-of-Medical-Things (IIoMT). The sixth generation (6G) technology is considered as the fabric that facilitates the realization of these technologies, creating a paradigm shift towards personalized e-health services. To deal with the high security requirements and the energy constraints of 6G-enabled e-health services, we propose a lightweight learning-based key generation scheme for a pair of wireless-powered nodes in a cooperative communication system, where the legitimate nodes and the intermediate node have low-cost hardware-impaired transceivers. We utilize an echo state network (ESN) to enhance the “randomness distillation” phase, in which the legitimate parties try to obtain a common source of randomness as the raw data for key agreement. The PHY-based observed data is passed to the ESN, containing a reservoir of sparsely connected neurons to compensate for observation mismatches caused by the unbalanced hardware impairments. The output of the ESN can then be utilized to extract the secret key between e-health endpoints. Numerical experiments verify the performance gain of our proposed echo-based approach, resulting in 50% less required inference time compared with a fully-connected neural network (FCNN). Moreover, a performance gain of about 32% in terms of mean-square error (MSE) is achieved compared with a conventional PHY-only scheme.