Integrated Super-resolution Sensing and Symbiotic Communication with 3D Sparse MIMO for Low-Altitude UAV Swarm

Abstract

Low-altitude unmanned aerial vehicle (UAV) swarms are expected to play important role for future intelligent aerial systems due to their great potential to cooperatively accomplish complicated missions effectively. However, there are important challenges to be addressed to enable their efficient operation: the large-scale nature of swarms usually leads to excessive spectrum consumption, and ultra-low cost requirements for individual UAVs renders it necessary to develop more cost-effective communication modules. In addition, the densely located swarm UAVs require high resolution for localization and sensing. To address the above challenges and simultaneously achieve spectrum and energy-efficient communication and accurate sensing, we investigate low-altitude UAV swarm with integrated super-resolution sensing and symbiotic communication technology. Specifically, one leading UAV may act as a primary transmitter (PT) to transmit communication signals to the base station (BS), and the remaining nearby UAVs in the swarm act as passive backscatter devices (BDs), which can modulate their information by efficiently backscattering the radio frequency (RF) signals from the PT without consuming extra spectrum or power. In addition, to achieve efficient three-dimensional (3D) super-resolution sensing for the densely located UAV swarm, 3D sparse multiple-input multiple-output (MIMO) technology and super-resolution signal processing algorithms are further exploited, where both L-shaped nested array (LNA) and planar nested arrays (PNA) are considered at the BS. To evaluate the communication and sensing performance for the UAV-symbiotic radio (SR) system, the achievable rates of UAV swarm are derived and the beam patterns of sparse LNA, PNA and the benchmarking compact uniform planar array (UPA) are compared.