Mambas: Maneuvering Analog Multi-User Beamforming using an Array of Subarrays in mmWave Networks

Abstract

Beyond-5G and 6G wireless networks exploit the millimeter-wave (mmWave) frequency bands to achieve significantly improved data rates, and existing mmWave systems rely on analog single-user beamforming (SUBF) or hybrid multi-user beamforming (MUBF). In this work, we focus on improving the performance of multi-user communication in mmWave networks by exploring analog MUBF using an array of subarrays (ASA) with reduced system overhead and hardware complexity as it eliminates digital beamforming and the need for estimating the channel state information (CSI). We present Mambas, a novel system that maneuvers analog MUBF using an ASA to support simultaneous communication with multiple users located in close proximity, e.g., within the half-power beamwidth of the ASA. In essence, Mambas effectively decouples the user selection, subarray allocation, and beamforming optimization based on a comprehensive understanding of the multi-user support determined by the ASA. We evaluate Mambas using a 28 GHz software-defined radio testbed and show that, compared to existing methods, Mambas can effectively support users that are 2× more closely spaced while achieving an improved sum rate of up to 2×, using only two subarrays. Large-scale ray tracing-based simulations also show that Mambas can achieve a sum rate gain of 1.92–3.86× and is able to maintain consistent performance with significantly increased user density.

Zhenzhou (Tom) Qi

Ph.D. Student at Duke University

My research interests include Next-G Wireless Communication Systems and IoT.