超越后向散射通信:超材料无源物联网感知新范式OA

With the increasing complexity and diversity of Internet of Things(IoT)application requirements,the number of sensors connected to the network is expected to grow exponentially.Among existing sensing technologies,passive sensing tags based on backscattering mechanism show the potential for large-scale deployments due to their extremely low cost and maintenance-free characteristics.However,the limited transmission distance of existing passive sensor tags restricts their widespread deployment in practical applications.To address this issue,a passive IoT sensing system based on meta-material tags is proposed.As a backscattering tag,it can change its scattering coefficient of wireless signals according to the state of the sensing target.The wireless transceiver can simultaneously sense and transmit by processing the scattered signal from the tag.The meta-material tag is composed of multiple units arranged at intervals of less than half a wavelength,which effectively concentrates the signal energy,facilitates the detection at the receiver,and further increases the transmission distance.In order to optimize the performance of the system,a novel joint sensing and transmission model is built based on the meta-material equivalent circuit model and wireless transmission model.Furthermore,we optimize the structure of the meta-material tag and propose a deep learning-based sensing algorithm to sense the distribution of environmental conditions.The prototype experiment realizes a humidity-sensitive tag based on a split-ring resonator and demonstrates the system's ability to achieve passive sensing of humidity distribution within a 2m range,which effectively extends the transmission distance of the passive sensing from the decimeter level to the meter level.