Jidong Tian, Yitian Li, Wenqing Chen, Liqiang Xiao, Hao He, Yaohui Jin. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.

Cross-technology Communication (CTC) is a promising paradigm for efficient coordination and cooperation among heterogeneous wireless technologies. Recent advances in physical-layer CTC (PHY-CTC) approaches the standards’ maximum transmission rate by exploring PHY-layer signal features. However, due to the lack of reliable feedback, current PHY-CTC technologies can hardly ensure transmission reliability. This paper presents RAP (Reliable Acknowledged PHY-CTC), a bidirectional CTC design with reliable PHY-CTC feedback. First, we present a novel PHY-CTC technique to efficiently establish a reliable feedback channel (e.g., ACKs or NACKs). Then, based on the feedback, we propose a joint intra-packet coding and inter-packet coding scheme to improve the reliability of CTC. Finally, we present an on-demand data (re)transmission scheme to support unicast, multicast and broadcast more efficiently. We implement and evaluate RAP on USRP N210 with IEEE 802.11g PHY (WiFi) and commodity ZigBee devices. The experiment results show RAP achieves reliable data transmission (>99% packet reception rate (PRR)) and high throughput (over 35kbps) under a wide range of scenarios.

Wenqing Chen, Jidong Tian, Yitian Li, Hao He, Yaohui Jin. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.