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In this paper, unmanned aerial vehicles (UAVs) and intelligent reflective surface (IRS) are utilized to support terahertz (THz) communications. To this end, the joint optimization of UAVs trajectory, the phase shift of IRS, the allocation of THz sub-bands, and the power control is investigated to maximize the minimum average achievable rate of all the users. An iteration algorithm based on successive Convex Approximation with the Rate constraint penalty (CAR) is developed to obtain UAVs trajectory, and the IRS phase shift is formulated as a closed-form expression with introduced pricing factors. Simulation results show that the proposed scheme significantly enhances the rate performance of the whole system.
Terahertz (THz) communications have emerged as a promising candidate to support the heavy data traffic and exploding network capacity in the future 6G wireless networks. However, THz communications are facing many challenges for practical implementat
An intelligent reflecting surface (IRS) at terahertz (THz) bands is expected to have a massive number of reflecting elements to compensate for the severe propagation losses. However, as the IRS size grows, the conventional far-field assumption starts
In this paper, we propose a deep reinforcement learning (DRL) approach for solving the optimisation problem of the networks sum-rate in device-to-device (D2D) communications supported by an intelligent reflecting surface (IRS). The IRS is deployed to
In this letter, we study the secure communication problem in the unmanned aerial vehicle (UAV) enabled networks aided by an intelligent reflecting surface (IRS) from the physical-layer security perspective. Specifically, the IRS is deployed to assi
Intelligent reflecting surface (IRS) has been recently employed to reshape the wireless channels by controlling individual scattering elements phase shifts, namely, passive beamforming. Due to the large size of scattering elements, the passive beamfo