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When the communication infrastructures are damaged in disasters, unmanned aerial vehicles (UAVs) can be utilized as aerial base stations to achieve rapid service recovery. However, the wireless coverage of a single UAV is limited, and device-to-device (D2D) transmission can be exploited to accommodate more users with wireless service. Thus, in this paper, we consider the user association for a dual-UAV-enabled wireless network with the help of D2D connections in disasters. To achieve better performance, we maximize the weighted sum rate of the UAV-served users and the total number of D2D-connected users by optimizing the user association. The formulated problem is a combinatorial optimization problem involving binary variables, which is extremely difficult to solve. Accordingly, we propose two algorithms to solve it approximatively. The first algorithm is the learning-based clustering algorithm by viewing the optimization as a clustering problem. The users who can be served by the UAVs are regarded as cluster centers, which need to be selected optimally. In the second one, the binary variables are relaxed into continuous variables, and then, the problem can be solved by the existing optimization tools. The simulation results demonstrate that these two algorithms can achieve excellent suboptimal performance, and the computational complexity of the learning-based clustering algorithm is much lower. INDEX TERMS Device-to-device (D2D), learning-based clustering algorithm, unmanned aerial vehicle (UAV), user association.

The cognitive sensor (CS) can transmit data to the control center in the same spectrum that is licensed to the primary user (PU) when the absence of the PU is detected by spectrum sensing. However, the battery energy of the CS is limited due to its small size, deployment in atrocious environments and long-term working. In this paper, an energy-harvesting-based CS is described, which senses the PU together with collecting the radio frequency energy to supply data transmission. In order to improve the transmission performance of the CS, we have proposed the joint resource allocation of spectrum sensing and energy harvesting in the cases of a single energy-harvesting-based CS and an energy-harvesting-based cognitive sensor network (CSN), respectively. Based on the proposed frame structure, we have formulated the resource allocation as a class of joint optimization problems, which seek to maximize the transmission rate of the CS by jointly optimizing sensing time, harvesting time and the numbers of sensing nodes and harvesting nodes. Using the half searching method and the alternating direction optimization, we have achieved the sub-optimal solution by converting the joint optimization problem into several convex sub-optimization problems. The simulation results have indicated the predominance of the proposed energy-harvesting-based CS and CSN models.