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

TL;DR

This paper presents a novel UAV-assisted framework employing clustering, beamforming, and deep reinforcement learning to enhance security in wireless networks against passive eavesdroppers with unknown CSI.

Contribution

It introduces an integrated approach combining clustering, multi-user beamforming, and DRL-based trajectory optimization for UAVs to improve secrecy capacity without eavesdropper CSI.

Findings

High secrecy capacities achieved with optimized UAV and GBS transmission parameters.

The approach scales well with increasing number of users.

Combines closed-form solutions with machine learning for trajectory and power optimization.

Abstract

This paper tackles the fundamental passive eavesdropping problem in modern wireless communications in which the location and the channel state information (CSI) of the attackers are unknown. In this regard, we propose deploying an unmanned aerial vehicle (UAV) that serves as a mobile aerial relay (AR) to help ground base station (GBS) support a subset of vulnerable users. More precisely, our solution (1) clusters the single-antenna users in two groups to be either served by the GBS directly or via the AR, (2) employs optimal multi-user beamforming to the directly served users, and (3) optimizes the AR's 3D position, its multi-user beamforming matrix and transmit powers by combining closed-form solutions with machine learning techniques. Specifically, we design a plain beamforming and power optimization combined with a deep reinforcement learning (DRL) algorithm for an AR to optimize its trajectory for the security maximization of the served users. Numerical results show that the multi-user multiple input, single output (MU-MISO) system split between a GBS and an AR with optimized transmission parameters without knowledge of the eavesdropping channels achieves high secrecy capacities that scale well with increasing the number of users.