This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). The selection of UAV relay nodes in IoV employs mechanisms executed either at centralized servers or decentralized nodes, which have two main limitations: 1) the traceability of the selection mechanism execution and 2) the coordination among the selected UAVs, which is currently offered in a centralized manner and is not coupled with the relay selection. Existing UAV coordination methods often rely on optimization methods, which are not adaptable to different environment complexities, or on centralized deep reinforcement learning, which lacks scalability in multi-UAV settings. Overall, there is a need for a comprehensive framework where relay selection and coordination are coupled and executed in a transparent and trusted manner. This work proposes a framework empowered by reinforcement learning and Blockchain for UAV-assisted IoV networks. It consists of three main components: a two-sided UAV relay selection mechanism for UAV-assisted IoV, a decentralized Multi-Agent Deep Reinforcement Learning (MDRL) model for autonomous UAV coordination, and a Blockchain implementation for transparency and traceability in the interactions between vehicles and UAVs. The relay selection considers the two-sided preferences of vehicles and UAVs based on the Quality-of-UAV (QoU) and the Quality-of-Vehicle (QoV). Upon selection of relay UAVs, the decentralized coordination between them is enabled through an MDRL model trained to control their mobility and maintain the network coverage and connectivity using Proximal Policy Optimization (PPO). The evaluation results demonstrate that the proposed selection and coordination mechanisms improve the stability of the selected relays and maximize the coverage and connectivity achieved by the UAVs.

Radio Frequency Energy Harvesting (RF-EH) networks are key enablers of massive Internet-ofthings by providing controllable and long-distance energy transfer to energy-limited devices. Relays, helping either energy or information transfer, have been demonstrated to significantly improve the performance of these networks. We first obtain the optimal solution to the scheduling and power control problem for the given relay selection. Then, the relay selection problem is formulated as a classification problem, for which two convolutional neural network (CNN) based architectures are proposed. While the first architecture employs conventional 2D convolution blocks and benefits from skip connections between layers; the second architecture replaces them with inception blocks, to decrease trainable parameter size without sacrificing accuracy for memory-constraint applications. To decrease the runtime complexity further, teacher-student learning is employed such that the teacher network is larger, and the student is a smaller size CNN-based architecture distilling the teacher's knowledge. A novel dichotomous searchbased algorithm is employed to determine the best architecture for the student network. Our simulation results demonstrate that the proposed solutions provide lower complexity than the state-of-art iterative approaches without compromising optimality. A. G. Onalan and S. Coleri are with the Department of Electrical and Electronics Engineering, and B.Kopru is with Department of Computer Engineering, Koc University, 34450, Istanbul, Turkey e-mail: {aonalan17, bkopru17, scoleri}@ku.edu.tr. Sinem Coleri acknowledges the support of the Scientific and Technological Research Council of Turkey 2247-A National Leaders Research Grant #121C314. Energy Harvesting (EH) is a promising alternative to fixed power supplies (e.g., batteries) for energy-constrained Internet-of-Things (IoT) applications, such as smart cities and wearable devices [1], by enabling the net-zero energy objective of sixth generation (6G) mobile networks [2]. The use cases and feasibility of wireless-powered IoT have been investigated in many standardization bodies, including IEEE 802.11 [3] and 3GPP [4]. Radio Frequency (RF) signals are preferred over other energy sources such as vibration and sun due to their independence from climate conditions, accessibility, and more predictable nature [5]. RF-EH network design follows two main protocols: Simultaneous Information and Power Transfer (SWIPT), in which power and information are sent simultaneously, and Wireless Powered Communication Networks (WPCN), in which power and information are sent consecutively in downlink (DL) and uplink (UL) [6].

Opportunistic relay selection (ORS) has been recognized as a simple but efficient method for mobile nodes to achieve cooperative diversity in slow fading channels. With the proliferation of high-mobility applications and the adoption of higher frequency bands in 5G and beyond systems, the problem of outdated CSI will become more serious. Therefore, the design of a novel cooperative method that is applicable to not only slow fading but also fast fading is increasingly of importance. To this end, we develop and analyze a deep-learning-aided cooperative method coined predictive relay selection (PRS) in this article. It can remarkably improve the quality of CSI through fading channel prediction while retaining the simplicity of ORS by selecting a single opportunistic relay so as to avoid the complexity of multi-relay coordination and synchronization. Information-theoretic analysis and numerical results in terms of outage probability and channel capacity reveal that PRS achieves full diversity gain in slow fading wireless environments and substantially outperforms the existing schemes in fast fading channels. N wireless communications [1], diversity is an important and essential technique, which can effectively combat the effect of multi-path channel fading by means of transmitting redundant signals over independent channels and then combining multiple faded copies at the receiver. Spatial diversity is particularly attractive as it can be easily combined with other forms of diversity and achieve higher diversity order by simply installing more antennas. Because of the constraint on power supply, hardware size, and cost, it is difficult for mobile terminals in cellular systems or wireless nodes in ad hoc networks to exploit spatial diversity at sub-6GHz carrier frequencies. W. Jiang is with German Research Centre for Artificial Intelligence (DFKI), Kaiserslautern, Germany, and is also with the University of Kaiserslautern, Germany, (e-mail: wei.jiang@dfki.de). H. D. Schotten is with German Research Centre for Artificial Intelligence (DFKI), Kaiserslautern, Germany, and is also with the University of Kaiserslautern, Germany, (e-mail: schotten@eit.uni-kl.de). In such a cooperative network, when a node sends a signal, its neighboring nodes could act as relays to decode-and-forward (DF) or amplify-and-forward (AF) this signal. By combining multiple copied versions of the original signal at the destination, the network achieves cooperative diversity that is equivalent to spatial diversity gained from co-located multi-antenna systems [4].