With the advantages of high flexibility and lightweight, unmanned aerial vehicles (UAVs) have been widely used in data collection of wireless sensor networks. For a multi-UAV-assisted wireless sensor network with randomly distributed and moved users, how to plan the flight paths of the UAVs to effectively collect data from the users remains a challenging problem. This paper aims to maximize the average throughput of data collection in a dynamic environment where the user's location cannot be predicted by optimizing the flight path of the UAVs, which is subject to the shortest flight time and range constraints of UAVs, the constraints of UAV start and end points, the communication distance constraints, the user communication constraints, and the UAV collision avoidance constraints. The resultant problem can be solved by using existing optimization methods with high complexity, which however is difficult to obtain the globally optimal solution. To address this problem efficiently, this paper proposes a deep reinforcement learning algorithm based on Dueling Double DQN (Dueling-DDQN) . The proposed algorithm adopts the Dueling network architecture, which enhances the learning ability of the algorithm and improves the robustness and convergence speed of tracked in suboptimal solutions due to the over-estimation on the $Q$ value. Simulation results show that the proposed algorithm can efficiently obtain the flight paths of multiple UAVs under all constraints . In particular, our proposed algorithm has encouraging convergence and stability performance in comparison with the existing benchmark algorithms.

The mobile ad-hoc network (MANET) under port yards relies heavily on the channel information between small communication nodes. However, it remains a challenging problem to deploy traditional large channel measurement equipment on small nodes. In addition, the wireless channel between nodes exhibits strong spatial and temporal variation characteristics. Consequently, conducting channel measurement and modeling in this scenario presents dual challenges of the equipment portability and channel modeling accuracy. To address the portability of measurement equipment, this paper investigates the highly portable and high-precision channel measurement method based on the universal software radio peripheral (USRP) and analyzes the influence of metal structures on the large-scale and small-scale parameters. To address the problem of low accuracy of the ray-tracing channel model due to the high complexity of the scenario, we propose an optimal parameter search method based on the channel measurement data by identifying the key simulation parameters of the ray-tracing channel model. By searching for the optimal values of the key simulation parameters, the channel model is iteratively calibrated. The experimental results show that our proposed model is suitable for fitting the path loss with a double-slope model in the MANET scenario, where the multipath effect caused by the metal structure results in a multi-peak distribution of the average power delay profile. The results also show that the proposed optimal parameter search method significantly improves the agreement between the simulation values of path loss and RMS delay spread with the measured values, such that the accuracy and universality of the channel model can be improved.

Intelligent reflecting surface (IRS) and cognitive simultaneous wireless information and power transfer (SWIPT) are regarded as potential key technologies to improve energy efficiency and spectrum utilization of wireless communication systems. This paper studied the IRS-aided cognitive SWIPT network based on a nonlinear energy harvesting model. In the network, a secondary transmitter simultaneously transmits information and energy to multiple secondary receivers, and each secondary receiver adopts the power splitting scheme to realize information decoding and energy harvesting. The aim is to minimize the transmit power of the secondary user transmitter by jointly optimizing the beamforming vector of the secondary transmitter, the power splitting coefficients of the secondary receivers, and the phase shifts of the IRS. In order to guarantee the information and energy transmission efficiency of the secondary users and limit the co-channel interference from the secondary users to the primary users, it is considered that the secondary receivers have the constraints of the minimum received signal-to-interference noise ratio, the minimum energy harvesting amount, and the values of power splitting coefficients, the secondary transmitter has the constraints of the maximum interference power values to the primary users, and the IRS has the constraints on its reflection phase shifts. The considered optimization problem is a non-convex quadratically constrained quadratic program problem with highly coupled optimization variables, which is difficult to solve. An alternating optimization algorithm based on the semidefinite relaxation and sequential rank one constraint relaxation techniques was proposed to solve the problem efficiently. In order to reduce the computation complexity, a low-complexity optimization algorithm based on IRS element grouping was further proposed. Simulation results show that compared to several benchmark algorithms, the proposed algorithms can effectively reduce the transmit power of the secondary transmitter.