60 GHz technology holds tremendous potential to upgrade wireless link throughput to Gbps level. To overcome inherent vulnerability to attenuation, 60 GHz radios communicate by forming highly-directional electronically-steerable beams. Standards like IEEE 802.11ad have tailored MAC/PHY protocols to such flexible-beam 60 GHz networks. However, lack of a reconfigurable platform has thwarted a realistic proof-of-concept evaluation. In this paper, we conduct an in-depth measurement of indoor 60 GHz networks using a first-of-its-kind software-radio platform. Our measurement focuses on the link-level behavior with three major perspectives: (i) coverage and bit-rate of a single link, and implications for 60 GHz MIMO; (ii) impact of beam-steering on network performance, particularly under human blockage and device mobility; (iii) spatial reuse between flexible beams. Our study dispels some common myths, and reveals key challenges in maintaining robust flexible-beam connection. We propose new principles that can tackle such challenges based on unique properties of 60 GHz channel and cognitive capability of 60 GHz links.

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High-quality, speaker-location-aware audio capturing has traditionally been realized using dedicated microphone arrays. But high cost and lack of portability prevents such systems from being widely adopted. Today's smartphones are relatively more convenient for audio recording, but the audio quality is much lower in noisy environment and speaker location cannot be readily obtained. In this paper, we design and implement Dia, which leverages smartphone cooperation to overcome the above limitations. Dia supports spontaneous setup, by allowing a group of users to rapidly assemble an array of smartphones to emulate a dedicated microphone array. It employs a novel framework to accurately synchronize the audio I/O clocks of the smartphones. The synchronized smartphone array further enables autodirective audio capturing, i.e., tracking the speaker's location, and beamforming the audio capturing towards the speaker to improve audio quality. We implement Dia on a testbed consisting of 8 Android phones. Our experiments demonstrate that Dia can synchronize the microphones of different smartphones with sample-level accuracy. It achieves high localization accuracy, and similar beamforming performance compared with a microphone array with perfect synchronization.