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

arXiv: https://arxiv.org/abs/2306.06686 · HTML: https://mlsanity.com/paper/2306.06686/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 63 references that are in the map.
- First posted on arXiv 11 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Deep Reinforcement Learning Based Parameter Control in Differential Evolution](https://mlsanity.com/paper/1905.08006/) (2019)
- [6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities](https://mlsanity.com/paper/2008.03213/) (2020)
- [Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges](https://mlsanity.com/paper/1809.01752/) (2018)
- [The Sky Is Not the Limit: LTE for Unmanned Aerial Vehicles](https://mlsanity.com/paper/1707.07534/) (2017)
- [UAV-Assisted Attack Prevention, Detection, and Recovery of 5G Networks](https://mlsanity.com/paper/2007.00244/) (2020)
- [Evolution of Q Values for Deep Q Learning in Stable Baselines](https://mlsanity.com/paper/2004.11766/) (2020)
- [Securing Mobile IoT with Unmanned Aerial Systems](https://mlsanity.com/paper/2005.07363/) (2020)
- [Securing Mobile Multiuser Transmissions with UAVs in the Presence of Multiple Eavesdroppers](https://mlsanity.com/paper/2009.03557/) (2020)
- [Open-Source Software Radio Platform for Research on Cellular Networked UAVs -- It Works!](https://mlsanity.com/paper/2112.09067/) (2021)
- [Performance Analysis of a Mission-Critical Portable LTE System in Targeted RF Interference](https://mlsanity.com/paper/1708.06814/) (2017)
- [Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach](https://mlsanity.com/paper/2112.11090/) (2021)

## Abstract (arXiv)

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.