  Millimeter-wave (mmWave) technology is pivotal for next-generation wireless
networks, enabling high-data-rate and low-latency applications such as
autonomous vehicles and XR streaming. However, maintaining directional mmWave
links in dynamic mobile environments is challenging due to mobility-induced
disruptions and blockage. While effective, the current 5G NR beam training
methods incur significant overhead and scalability issues in multi-user
scenarios. To address this, we introduce CommRad, a sensing-driven solution
incorporating a radar sensor at the base station to track mobile users and
maintain directional beams even under blockages. While radar provides
high-resolution object tracking, it suffers from a fundamental challenge of
lack of context, i.e., it cannot discern which objects in the environment
represent active users, reflectors, or blockers. To obtain this contextual
awareness, CommRad unites wireless sensing capabilities of bi-static radio
communication with the mono-static radar sensor, allowing radios to provide
initial context to radar sensors. Subsequently, the radar aids in user tracking
and sustains mobile links even in obstructed scenarios, resulting in robust and
high-throughput directional connections for all mobile users at all times. We
evaluate this collaborative radar-radio framework using a 28 GHz mmWave testbed
integrated with a radar sensor in various indoor and outdoor scenarios,
demonstrating a 2.5x improvement in median throughput compared to a
non-collaborative baseline.
