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Abstract-In cognitive radio systems, in order for the secondary users to opportunistically share the spectrum without interfering the primary users, an accurate spectrum measurement and a precise estimation of the interference at the primary users are necessary but are challenging tasks. Since it is impractical in cognitive radio systems to assume that the channel state information of the interference link is available at the cognitive transmitter, the interference at the primary users is hard to be estimated accurately. This paper introduces a resource allocation algorithm for OFDMA-based cognitive radio systems, which utilizes location information of the primary and secondary users instead of the channel state information of the interference link. Simulation results show that it is indeed effective to incorporate location information into resource allocation so that a nearoptimal capacity is achieved.

Unmanned aerial vehicles (UAVs) have gained a lot of popularity in diverse wireless communication fields. They can act as high-altitude flying relays to support communications between ground nodes due to their ability to provide line-of-sight links. With the flourishing Internet of Things, several types of new applications are emerging. In this paper, we focus on bandwidth hungry and delay-tolerant applications where multiple pairs of transceivers require the support of UAVs to complete their transmissions. To do so, the UAVs have the possibility to employ two different bands namely the typical microwave and the highrate millimeter wave bands. In this paper, we develop a generic framework to assign UAVs to supported transceivers and optimize their trajectories such that a weighted function of the total service time is minimized. Taking into account both the communication time needed to relay the message and the flying time of the UAVs, a mixed non-linear programming problem aiming at finding the stops at which the UAVs hover to forward the data to the receivers is formulated. An iterative approach is then developed to solve the problem. First, a mixed linear programming problem is optimally solved to determine the path of each available UAV. Then, a hierarchical iterative search is executed to enhance the UAV stops' locations and reduce the service time. The behavior of the UAVs and the benefits of the proposed framework are showcased for selected scenarios.Index Terms-3D positioning and path planning, cooperative networks, mmWave communications, unmanned aerial vehicles.

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