In this paper, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BS) and cellular-connected drone users (drone-UEs), is introduced. For this new 3D cellular architecture, a novel framework for network planning for drone-BSs as well as latency-minimal cell association for drone-UEs is proposed. For network planning, a tractable method for drone-BSs' deployment based on the notion of truncated octahedron shapes is proposed that ensures full coverage for a given space with minimum number of drone-BSs. In addition, to characterize frequency planning in such 3D wireless networks, an analytical expression for the feasible integer frequency reuse factors is derived. Subsequently, an optimal 3D cell association scheme is developed for which the drone-UEs' latency, considering transmission, computation, and backhaul delays, is minimized. To this end, first, the spatial distribution of the drone-UEs is estimated using a kernel density estimation method, and the parameters of the estimator are obtained using a cross-validation method. Then, according to the spatial distribution of drone-UEs and the locations of drone-BSs, the latency-minimal 3D cell association for drone-UEs is derived by exploiting tools from optimal transport theory. Simulation results show that the proposed approach reduces the latency of drone-UEs compared to the classical cell association approach that uses a signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the proposed approach yields a reduction of up to 46% in the average latency compared to the SINR-based association. The A preliminary conference version of this work appears in [1].Recent reports from the federal aviation administration (FAA) shows that the number of unmanned aerial vehicles (UAVs), also known as drones, will exceed 7 million in 2020 [2]. Such a massive use of drones will have significant impacts on wireless networking. From a wireless perspective, the two key roles of drones include: aerial base station (BS), and user equipment (UE) [3]-[5]. Due to their flexibility and inherent ability for line-of-sight (LoS) communications, drone-BSs can provide broadband, wide-scale, and reliable wireless connectivity during disasters and temporary events [4]-[11]. Moreover, drone-BSs offer a promising solution for ultra-flexible deployment and cost-effective wireless services, without the prohibitive costs of terrestrial BSs.Meanwhile, drones can also act as UEs (i.e., cellular-connected drone-UEs) that must connect to a wireless network so as to operate. In particular, cellular-connected drone-UEs can be used for wide range of applications such as package delivery [12], surveillance, remote sensing, and virtual reality. The key feature of drone-UEs is their ability to intelligently move in three dimensions and optimize their trajectory in order to efficiently complete their missions. Therefore, drone-UEs are widely used for delivery purposes such as in Amazon's prime air drone delivery service and drug delivery in me...

When deployed as reflectors for existing wireless base stations (BSs), reconfigurable intelligent surfaces (RISs) can be a promising approach to achieve high spectrum and energy efficiency. However, due to the large number of RIS elements, the joint optimization of the BS and reflector RIS configuration is challenging. In essence, the BS transmit power and RIS's reflecting configuration must be optimized so as to improve users' data rates and reduce the BS power consumption. In this paper, the problem of energy efficiency optimization is studied in an RIS-assisted cellular network endowed with an RIS reflector powered via energy harvesting technologies. The goal of this proposed framework is to maximize the average energy efficiency by enabling a BS to determine the transmit power and RIS configuration, under uncertainty on the wireless channel and harvested energy of the RIS system. To solve this problem, a novel approach based on deep reinforcement learning is proposed, in which the BS receives the state information, consisting of the users' channel state information feedback and the available energy reported by the RIS. Then, the BS optimizes its action composed of the BS transmit power allocation and RIS phase shift configuration using a neural network. Due to the intractability of the formulated problem under uncertainty, a case study is conducted to analyze the performance of the studied RIS-assisted downlink system by asymptotically deriving the upper bound of the energy efficiency. Simulation results show that the proposed framework improves energy efficiency up to 77.3% when the number of RIS elements increases from 9 to 25.

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