Trajectory optimization of UAV-IRS assisted 6G THz network using deep reinforcement learning approach.

Terahertz (THz) wireless communication is a promising technology that will enable ultra-high data rates, and very low latency for future wireless communications. Intelligent Reconfigurable Surfaces (IRS) aiding Unmanned Aerial Vehicle (UAV) are two essential technologies that play a pivotal role in balancing the demands of Sixth-Generation (6G) wireless networks. In practical scenarios, mission completion time and energy consumption serve as crucial benchmarks for assessing the efficiency of UAV-IRS enabled THz communication. Achieving swift mission completion requires UAV-IRS to fly at maximum speed above the ground users it serves. However, this results in higher energy consumption. To address the challenge, this paper studies UAV-IRS trajectory planning problems in THz networks. The problem is formulated as an optimization problem aiming to minimize UAVs-IRS mission completion time by optimizing the UAV-IRS trajectory, considering the energy consumption constraint for UAVs-IRS. The proposed optimization algorithm, with low complexity, is well-suited for applications in THz communication networks. This problem is a non-convex, optimization problem that is NP-hard and presents challenges for conventional optimization techniques. To overcome this, we proposed a Deep Q-Network (DQN) reinforcement learning algorithm to enhance performance. Simulation results show that our proposed algorithm achieves performance compared to benchmark schemes.

Beam-steering of dielectric flat lens nanoantenna with elliptical patch based on antenna displacement for optical wireless applications.

In this paper, the switched-beam nanoantenna (NA) concept is introduced with a theoretical design of an inhomogeneous dielectric flat lens modelled with different materials to steer and enhance the radiation in a particular direction based on shifting the illuminator element. Firstly, the design of hybrid plasmonic NA is introduced and analyzed considering different silicon patch shapes such as rectangular, circular, hexagonal, and elliptical shapes. The elliptical patch NA achieves a gain of up to 10.7 dBi and a return loss of - 14.41 dB. Then the design of a gradient-index dielectric flat lens with the NA is introduced to improve the antenna performance by increasing the directivity and consequently decreasing the beam-width. Furthermore, the beam-steering capabilities by displacement of the NA according to different feeding points along the X and Y-direction. By using the gradient-index dielectric flat lens, the gain is increased to 18.4 dBi with an improvement in the return loss reach to - 19.15 dB compared with traditional NA. In addition, the beam-steering capabilities were achieved with a range ± 60° × ± 55° with acceptable average antenna gain, side-lobe levels, and half power beam-width of 16.5 dBi, - 12.3 dB and 13.6° respectively.