Alternating Iterative Secure Structure between Beamforming and Power Allocation for UAV-aided Directional Modulation Networks

TL;DR

This paper proposes an alternating iterative structure to optimize beamforming and power allocation in UAV-aided directional modulation networks, significantly enhancing secrecy rate performance.

Contribution

It introduces a novel AIS method that iteratively optimizes beamforming and power allocation, leading to rapid convergence and substantial SR gains, especially in small-scale antenna arrays.

Findings

AIS converges rapidly in simulations.

Achieves significant SR improvements over fixed PA strategies.

Performance approaches an SR ceiling as antenna scale increases.

Abstract

In unmanned aerial vehicle (UAV) networks, directional modulation (DM) is adopted to improve the secrecy rate (SR) performance. Alice, a ground base station, behaves as a control center, and Bob is a UAV of flying along a linear flight trajectory who optimizes its SR performance by dynamically adjusting its beamforming vectors and power allocation (PA) strategy. Per fixed time interval during the Bob's flight process, the transmit beamforming vectors for useful messages and AN projection are given by the rule of maximizing signal-to-leakage-and-noise ratio (Max-SLNR) and maximizing AN-and-leakage-to-noise ratio (ANLNR), and the optimal PA strategy is based on maximizing SR (Max-SR). More importantly, an alternating iterative structure (AIS) between beamforming and PA is proposed to further improve the SR performance. Simulation results show that the proposed AIS converges rapidly, and can achieve substantial SR gains over Max-SLNR plus Max-ANLNR with fixed PA such as PA factor $\beta=$ 0.5, and 0.9. In particular, in the case of small-scale antenna array, the SR performance gain achieved by the proposed AIS is more attractive. Additionally, as the number of antennas tends to be large-scale, the average SR performance of the proposed AIS approaches an SR ceil.