Autonomous Environment Mapping Using Commodity Millimeter-wave Network Device

Ambient environment information,including reflectors' location,dimension and reflectivity,is a key input to many millimeter-wave(mmWave)networking and sensing applications.It has found versatile applications in optimizing network coverage and robustness,enhancing mobile link performance,and enabling high-accuracy indoor localization and navigation.Recent approaches of deriving mmWave environment information require heavy infrastructure support or non-trivial human labor,and rely on costly software defined radios,which prevent their usage in practice.In this work,we design and implement mmRanger,a system can automatically sense environment without any infrastructure support.mmRanger equips a pair of low-cost off-the-shelf mmWave radios in a commodity robot,which constantly samples the ambient environment by exchanging a series of mmWave signals while it moves and rotates.mmRanger then re-engineers the time-domain signal series to derive the spatial-domain environment structure,through novel reflection path extraction and reflector mapping algorithms.Our experiments verify that mmRanger can accurately and efficiently sense a given environment with minimal overhead,and the learned information can bring 1.6×and 2.1×performance gain,in terms of network coverage and mobile link throughput,respectively,over empirical approaches in mmWave networks.