Cross-Technology Communication (CTC) is an emerging technology to support direct communication between wireless devices that follow different standards. In spite of the many different proposals from the community to enable CTC, the performance aspect of CTC is an equally important problem but has seldom been studied before. We find this problem is extremely challenging, due to the following reasons: on one hand, a link for CTC is essentially different from a conventional wireless link. The conventional link indicators like RSSI (received signal strength indicator) and SNR (signal to noise ratio) cannot be used to directly characterize a CTC link. On the other hand, the indirect indicators like PER (packet error rate), which is adopted by many existing CTC proposals, cannot capture the short-term link behavior. As a result, the existing CTC proposals fail to keep reliable performance under dynamic channel conditions. In order to address the above challenge, we in this paper propose AdaComm, a generic framework to achieve self-adaptive CTC in dynamic channels. Instead of reactively adjusting the CTC sender, AdaComm adopts online learning mechanism to adaptively adjust the decoding model at the CTC receiver. The self-adaptive decoding model automatically learns the effective features directly from the raw received signals that are embedded with the current channel state. With the lossless channel information, AdaComm further adopts the fine tuning and full training modes to cope with the continuous and abrupt channel dynamics. We implement AdaComm and integrate it with two existing CTC approaches that respectively employ CSI (channel state information) and RSSI as the information carrier. The evaluation results demonstrate that AdaComm can significantly reduce the SER (symbol error rate) by 72.9% and 49.2%, respectively, compared with the existing approaches.
翻译:跨技术通信(CTC)是一种新兴技术,用于支持符合不同标准的无线装置之间的直接通信。尽管社区提出了许多不同的建议,以使CTC能够使用,但CTC的业绩方面是一个同样重要的问题,但以前很少研究过。我们发现这个问题极具挑战性,原因如下:一方面,CTC的链接与传统的无线链接基本不同。SRSI(接收信号强度指标)和SNR(信号到噪音比率)等常规链接指标无法直接用来描述CTC链接的特点。另一方面,许多现有的CTC建议采用的PER(包装误差率)等间接指标无法反映短期联系的行为。结果之一是,现有的CTC建议无法在动态频道条件下保持可靠的业绩。为了应对上述挑战,我们本文建议AdaCommal(一个通用框架,在动态的频道中实现自我适应性气候技术中心发送器,AdaCommal采用在线学习机制,以适应化地进一步调整CTC接收器的解析模型。9 现有PER(组合误差率率), 与当前Scial-commal Streal Indal ASy Adal Instry Adal 一起, 学习了当前Sildal demodal 。我们用智能化的自我-commal demodududududududududududududududududududududude dal 。