Zhang, Yu; Masouros, Christos; Xu, Tongyang; (2025) Zero-Power Integrated Sensing and Communication in Smart Healthcare Environments. IEEE Transactions on Cognitive Communications and Networking p. 1. 10.1109/tccn.2025.3573413. (In press).

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Abstract

In this paper, we propose a unified framework for integrated sensing and communication (ISAC) that utilizes received signal strength (RSS) measurements from zero-power backscatter radio frequency identification (RFID) tags for both communication and sensing. The proposed system employs a passive tag array, which can facilitate RSS-based key generation for resilient communication and healthcare sensing for fall detection. For key generation, we propose a time-series peak pattern coding method that can achieve strong randomness and low bit mismatch rate between two users, while for fall detection, we propose both a threshold-based approach and a spatio-temporal graph neural network (ST-GNN) method. We present experiments conducted in an indoor laboratory to collect data and validate the effectiveness of the proposed methods. The results demonstrate that the ST-GNN significantly outperforms traditional threshold-based techniques, achieving over 95% accuracy in fall detection across various datasets. Moreover, our backscatter ISAC system provides a cost-effective and sustainable solution for smart healthcare environments.

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