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Recently, intelligent reflecting surface (IRS) and unmanned aerial vehicle(UAV) have been introduced into wireless communication systems to enhance theperformance of air-ground transmission. To make a good balance betweenperformance, cost, and power consumption, a hybrid-IRS-and-UAV-assisteddirectional modulation (DM) network is investigated in this paper, where thehybrid IRS consists of passive and active reflecting elements. To maximize theachievable rate, three optimization algorithms, called maximum signal-to-noiseratio (SNR)-fractional programming (FP) (Max-SNR-FP), maximum SNR-equalamplitude reflecting (EAR) (Max-SNR-EAR), and maximumSNR-majorization-minimization (MM) (Max-SNR-MM), are proposed to jointly designthe beamforming vector and phase shift matrix (PSM) of hybrid IRS byalternately optimizing one and giving another. The Max-SNR-FP method employsthe successive convex approximation and FP methods to derive the beamformingvector and hybrid IRS PSM. The Max-SNR-EAR method adopts the maximumsignal-to-leakage-noise ratio method and the criteria of phase alignment andEAR to design them. In addition, the Max-SNR-MM method utilizes the MMcriterion to derive the IRS PSM. Simulation results show that the ratesharvested by the proposed three methods are slightly lower than those of activeIRS with higher power consumption, which are 35 percent higher than those of noIRS and random phase IRS, while passive IRS achieves only about 17 percent rategain over the latter. Moreover, compared to Max-SNR-FP, the proposedMax-SNR-EAR and Max-SNR-MM methods make an obvious complexity degradation atthe price of a slight performance loss.

Joint Beamforming and Phase Shift Design for Hybrid-IRS-and-UAV-aided Directional Modulation Network