We implement several low-cost ambient backscatter communication (AmBC) receivers for the first time in practice. Multi-antenna AmBC receivers have been actively investigated theoretically. Practical implementations face a challenge as low-cost antennas are prone to suffer from wideband effect and/or hardware impairments and propagation environment is complicated. For a successful implementation, we propose a signal processing method which avoid the wideband signal but extracts the reference signal which is narrowband. We evaluate the method using the data measured in a complicated environment. Measurement results show that the proposed signal processing tackles the above issues and gives acceptable bit-error-rate (BER) performance of AmBC systems. The implementation of low-cost multi-antenna AmBC receivers paves the way for realizing ubiquitous green communication systems.

Recognition of the context of humans plays an important role in pervasive applications such as intrusion detection, human density estimation for heating, ventilation and air-conditioning in smart buildings, as well as safety guarantee for workers during human-robot interaction. Radio vision is able to provide these sensing capabilities with low privacy intrusion. A common challenge though, for current radio sensing solutions is to distinguish simultaneous movement from multiple subjects. We present an approach that exploits antenna installations, for instance, found in upcoming 5G technology, to detect and extract activities from spatially scattered human targets in an ad-hoc manner in arbitrary environments and without prior training of the multi-subject detection. We perform receiver-side beamforming and beam-sweeping over different azimuth angles to detect human presence in those regions separately. We characterize the resultant fluctuations in the spatial streams due to human influence using a case study and make the traces publicly available. We demonstrate the potential of this approach through two applications: 1) By feeding the similarities of the resulting spatial streams into a clustering algorithm, we count the humans in a given area without prior training. (up to 6 people in a 22.4 m2area with an accuracy that significantly exceeds the related work). 2) We demonstrate that simultaneously conducted activities and gestures can be extracted from the spatial streams through blind source separation.