UAV Trajectory Design and Power Optimization for Terahertz Band-Integrated Sensing and Communications

Abstract

Sixth generation (6G) wireless networks require very low latency and an ultra-high data rate, which have become the main challenges for future wireless communications. To effectively balance the requirements of 6G and the extreme shortage of capacity within the existing wireless networks, sensing-assisted communications in the terahertz (THz) band with unmanned aerial vehicles (UAVs) is proposed. In this scenario, the THz-UAV acts as an aerial base station to provide information on users and sensing signals and detect the THz channel to assist UAV communication. However, communication and sensing signals that use the same resources can cause interference with each other. Therefore, we research a cooperative method of co-existence between sensing and communication signals in the same frequency and time allocation to reduce the interference. We then formulate an optimization problem to minimize the total delay by jointly optimizing the UAV trajectory, frequency association, and transmission power of each user. The resulting problem is a non-convex and mixed integer optimization problem, which is challenging to solve. By resorting to the Lagrange multiplier and proximal policy optimization (PPO) method, we propose an overall alternating optimization algorithm to solve this problem in an iterative way. Specifically, given the UAV location and frequency, the sub-problem of the sensing and communication transmission powers is transformed into a convex problem, which is solved by the Lagrange multiplier method. Second, in each iteration, for given sensing and communication transmission powers, we relax the discrete variable to a continuous variable and use the PPO algorithm to tackle the sub-problem of joint optimization of the UAV location and frequency. The results show that the proposed algorithm reduces the delay and improves the transmission rate when compared with the conventional greedy algorithm.

Trajectory Optimization of UAV-IRS Assisted 6G THz Network Using Deep Reinforcement Learning Approach

“…Furthermore, we considering the urban scenario with area 50 m× 50 m, including one BS located in middle area which can serving 50 GUs. The locations of the GUs are uniformly distributed within the circle coverage area with radius r = 6 m, in each episode, the GUs locations assumed to be fixed, other parameters are shown in Table 1 [25], and the settings parameters of network model are given in Table 2 [36]. To evaluate the proposed UAVs-IRS trajectory algorithm performance, we compare and implement the following three benchmark schemes in a clear manner.…”

“…In [24] the authors consider a Federated learning (FL) network in IRS-assisted UAV communications, in order for minimize the worst case mean square error (MSE) by jointly optimizing the UAV trajectory the IRS phase shift, and the user's transmission power. The authors in [25] jointly optimized flight trajectory of UAV and transmission power for each user in THz assist UAV communication to minimize the total the system delay, using DRL with primal proximal policy algorithm.…”