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Voice assistants such as Amazon Echo (Alexa) and Google Home use microphone arrays to estimate the angle of arrival (AoA) of the human voice. This paper focuses on adding user localization as a new capability to voice assistants. For any voice command, we desire Alexa to be able to localize the user inside the home. The core challenge is two-fold: (1) accurately estimating the AoAs of multipath echoes without the knowledge of the source signal, and (2) tracing back these AoAs to reverse triangulate the user's location.We develop VoLoc, a system that proposes an iterative align-and-cancel algorithm for improved multipath AoA estimation, followed by an error-minimization technique to estimate the geometry of a nearby wall reflection. The AoAs and geometric parameters of the nearby wall are then fused to reveal the user's location. Under modest assumptions, we report localization accuracy of 0.44 m across different rooms, clutter, and user/microphone locations. VoLoc runs in near real-time but needs to hear around 15 voice commands before becoming operational. 

Millimeter Wave (mmWave) networks can deliver multi-Gbps wireless links that use extremely narrow directional beams. This provides us with a new opportunity to exploit spatial reuse in order to scale network throughput. Exploiting such spatial reuse, however, requires aligning the beams of all nodes in a network. Aligning the beams is a difficult process which is complicated by indoor multipath, which can create interference, as well as by the inefficiency of carrier sense at detecting interference in directional links. This paper presents BounceNet, the first many-to-many millimeter wave beam alignment protocol that can exploit dense spatial reuse to allow many links to operate in parallel in a confined space and scale the wireless throughput with the number of clients. Results from three millimeter wave testbeds show that BounceNet can scale the throughput with the number of clients to deliver a total network data rate of more than 39 Gbps for 10 clients, which is up to 6.6× higher than current 802.11 mmWave standards.