4.7 Article

Securing the UAV-Aided Non-Orthogonal Downlink in the Face of Colluding Eavesdroppers

Journal

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 71, Issue 6, Pages 6837-6842

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2022.3160552

Keywords

Downlink; Autonomous aerial vehicles; Resource management; Security; Eavesdropping; Throughput; Receivers; Artificial noise; colluding eavesdroppers; non-orthogonal transmission; secure transmission; Unmanned aerial vehicle

Funding

  1. National Natural Science Foundation of China (NSFC) [61901254]
  2. Aeronautical Science Foundation of China [2020Z0660S6001]
  3. Deanship of Research Oversight and Coordination (DROC) at KFUPM under the Interdisciplinary Research Center for Communication Systems and Sensing [INCS2110]
  4. European Research Council's Advanced Fellow Grant QuantCom [789028]
  5. European Research Council (ERC) [789028] Funding Source: European Research Council (ERC)

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This study investigates non-orthogonal UAV-aided secure downlink transmissions in the presence of multiple colluding eavesdroppers, optimizing UAV trajectory, transmit power, and power splitting ratio to maximize secrecy rate. An iterative algorithm is proposed based on block coordinate descent and successive convex approximation techniques, demonstrating superiority in numerical results.
Non-orthogonal unmanned aerial vehicle (UAV)-aided secure downlink transmissions are investigated. A single-antenna UAV serves multiple ground users in the face of multiple colluding eavesdroppers (EVs) and only imperfect location information of the EVs is available. Specially, a power splitting based secure non-orthogonal downlink transmission scheme is considered, where the transmit power is divided into two parts for transmitting confidential information and artificial noise. Explicitly, we maximize the minimum average secrecy rate among all the users by optimizing the UAV trajectory, the transmit power and the power splitting ratio. In order to tackle this non-convex optimization problem, we propose an iterative algorithm based on the block coordinate descent and successive convex approximation techniques. Numerical results demonstrate the superiority of our proposed algorithm.

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