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Cross-Technology Communication (CTC) is an emerging technology to supportdirect communication between wireless devices that follow different standards.In spite of the many different proposals from the community to enable CTC, theperformance aspect of CTC is an equally important problem but has seldom beenstudied before. We find this problem is extremely challenging, due to thefollowing reasons: on one hand, a link for CTC is essentially different from aconventional wireless link. The conventional link indicators like RSSI(received signal strength indicator) and SNR (signal to noise ratio) cannot beused to directly characterize a CTC link. On the other hand, the indirectindicators like PER (packet error rate), which is adopted by many existing CTCproposals, cannot capture the short-term link behavior. As a result, theexisting CTC proposals fail to keep reliable performance under dynamic channelconditions. In order to address the above challenge, we in this paper proposeAdaComm, a generic framework to achieve self-adaptive CTC in dynamic channels.Instead of reactively adjusting the CTC sender, AdaComm adopts online learningmechanism to adaptively adjust the decoding model at the CTC receiver. Theself-adaptive decoding model automatically learns the effective featuresdirectly from the raw received signals that are embedded with the currentchannel state. With the lossless channel information, AdaComm further adoptsthe fine tuning and full training modes to cope with the continuous and abruptchannel dynamics. We implement AdaComm and integrate it with two existing CTCapproaches that respectively employ CSI (channel state information) and RSSI asthe information carrier. The evaluation results demonstrate that AdaComm cansignificantly reduce the SER (symbol error rate) by 72.9% and 49.2%,respectively, compared with the existing approaches.

AdaComm: Tracing Channel Dynamics for Reliable Cross-Technology Communication