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This paper tackles the fundamental passive eavesdropping problem in modernwireless communications in which the location and the channel state information(CSI) of the attackers are unknown. In this regard, we propose deploying anunmanned aerial vehicle (UAV) that serves as a mobile aerial relay (AR) to helpground base station (GBS) support a subset of vulnerable users. More precisely,our solution (1) clusters the single-antenna users in two groups to be eitherserved by the GBS directly or via the AR, (2) employs optimal multi-userbeamforming to the directly served users, and (3) optimizes the AR's 3Dposition, its multi-user beamforming matrix and transmit powers by combiningclosed-form solutions with machine learning techniques. Specifically, we designa plain beamforming and power optimization combined with a deep reinforcementlearning (DRL) algorithm for an AR to optimize its trajectory for the securitymaximization of the served users. Numerical results show that the multi-usermultiple input, single output (MU-MISO) system split between a GBS and an ARwith optimized transmission parameters without knowledge of the eavesdroppingchannels achieves high secrecy capacities that scale well with increasing thenumber of users.

UAV Trajectory and Multi-User Beamforming Optimization for Clustered Users Against Passive Eavesdropping Attacks With Unknown CSI