Beam-forecast: Facilitating mobile 60 GHz networks via model-driven beam steering
作者: Anfu Zhou, Xinyu Zhang, Huadong Ma
摘要
Low robustness under mobility is the Achilles' heel of the emerging 60 GHz networking technology. Instead of using omni-directional antennas as in existing Wi-Fi/cellular networks, 60 GHz radios communicate via highly-directional links formed by phased-array beam-forming, so as to upgrade wireless link throughput to multi-Gbps. However, user motion causes misalignment between the Tx's and Rx's beam directions, and often leads to link outage. Legacy 60 GHz protocols realign the beams by scanning alternative Tx/Rx beams. But unfortunately this tedious process can easily overwhelm the useful channel time, leaving the Tx/Rx in misalignment most of the time during mobility. In this paper, we propose Beam-forecast, a novel model-driven beam steering approach that can sustain high performance for mobile 60 GHz links. Beam-forecast is built on the observation that 60 GHz channel profiles at nearby locations are highly-correlated. By exploiting this correlation, Beam-forecast can reconstruct the channel profile as the Tx/Rx moves, without explicit channel scanning. In this way, it can predict new optimal beams and realign links for mobile users with minimal overhead. We evaluate Beam-forecast using a reconfigurable 60 GHz testbed along with a trace-driven simulator. Our experiments demonstrate multi-fold throughput gain compared with state-of-the-art under various practical scenarios.