Joint Channel Estimation and Radar Sensing for UAV Networks with mmWave Massive MIMO

In this paper, we study the application of integrated sensing and communication (ISAC) to unmanned aerial vehicle (UAV) networks aided by millimeter-wave (mmWave) massive multiple-input multiple-output (mMIMO). To reduce the pilot overhead for joint channel estimation (CE) and radar sensing, the state-of-the-art compressive sensing (CS) is applied to ISAC processing in UAV networks. Specifically, we proposed a full duplex terrestrial station architecture with hybrid beamforming (HBF), which can simultaneously communicates with UAVs and senses the surrounding environment to avoid UAV collisions. Given that the switch of phase shifter will take non-negligible reconfiguring time in HBF architecture, we propose a pilot waveform design which takes into account both CS theories and hardware constraints. We also design the mixed-resolution (MR) dictionaries that serve as the building block for formulating the joint CE and radar sensing as sparse signal recovery problems. On this basis, the MR orthogonal matching pursuit (MR-OMP) algorithm is utilized to effectively solve the problems. Simulation results demonstrate the good performances of both CE and radar sensing under the proposed ISAC framework.