Adaptive Dual-Beam Tracking for IRS-Assisted High-Speed Multi-UAV Communication Networks

TL;DR

This paper proposes an adaptive dual-beam tracking method to improve communication reliability in high-speed drone networks using intelligent reflecting surfaces.

Contribution

The novel adaptive dual-beam tracking scheme uses machine learning and optimization to enhance communication performance in high-speed multi-UAV networks.

Findings

An attention-based LSTM network accurately predicts UAV spatial angles for optimal beam coverage.

The proposed scheme maximizes the worst-case UAV’s SINR through joint optimization of beam components and IRS phase shifts.

Numerical results show improved performance and reliability across high-speed multi-UAV communication networks.

Abstract

This study investigates the communication network (MUAVN) of intelligent reflecting surface (IRS)-assisted high-speed multiple unmanned aerial vehicles, considering that highly dynamic UAVs may incur poor performance due to severe channel fading and rapid channel changes. Our objective is to design an adaptive dual-beam tracking scheme that mitigates beam misalignment, enhances the performance of the worst-case UAV, and sustains reliable communication links in the high-speed MUAVNs (HSMUAVNs). We first exploit an attention-based double-layer long short-term memory network to predict the spatial angle information of each UAV, which yields optimal beam coverage that matches to the UAV’s actual flight trajectory. Then, a worst-case UAV’s received beam components signal-to-interference plus noise ratio (SINR) maximization problem is formulated by jointly optimizing ground base station’s beam components and IRS’s phase shift matrix. To address this challenging problem, we decouple the optimization problem into two subproblems, which are then solved by leveraging semi-definite relaxation, the bisection method, and eigenvalue decomposition techniques. Finally, the adaptive dual beams are generated by linearly weighting the obtained beam components, each of which is well-matched to the corresponding moving UAV. Numerical results reveal that the proposed beam tracking scheme not only enhances the worst-case UAV’s performance but also guarantees a sufficient SINR demanded across the entire HSMUAVN.

Figures