Skin-MIMO: Vibration-based MIMO Communication over Human Skin
Dong Ma, Yuezhong Wu, Ming Ding, Mahbub Hassan, Wen Hu

TL;DR
This paper demonstrates the feasibility of MIMO communication over human skin using vibrations, and introduces a deep learning-based method to predict channel state information from inertial sensors, significantly enhancing capacity.
Contribution
The paper presents Skin-MIMO, a novel deep learning approach for CSI prediction in vibration-based skin communication, eliminating the need for channel sounding.
Findings
Skin-MIMO improves MIMO capacity by 2.3 times over SISO.
Gyroscope data outperforms accelerometer in predicting skin vibrations.
Feasibility of vibration-based MIMO communication over human skin is confirmed.
Abstract
We explore the feasibility of Multiple-Input-Multiple-Output (MIMO) communication through vibrations over human skin. Using off-the-shelf motors and piezo transducers as vibration transmitters and receivers, respectively, we build a 2x2 MIMO testbed to collect and analyze vibration signals from real subjects. Our analysis reveals that there exist multiple independent vibration channels between a pair of transmitter and receiver, confirming the feasibility of MIMO. Unfortunately, the slow ramping of mechanical motors and rapidly changing skin channels make it impractical for conventional channel sounding based channel state information (CSI) acquisition, which is critical for achieving MIMO capacity gains. To solve this problem, we propose Skin-MIMO, a deep learning based CSI acquisition technique to accurately predict CSI entirely based on inertial sensor (accelerometer and gyroscope)…
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Taxonomy
TopicsMillimeter-Wave Propagation and Modeling · Wireless Body Area Networks · Indoor and Outdoor Localization Technologies
