@article{lee_sun_kim_kim_shin_kim_kim_2023, title={Multi-Agent Reinforcement Learning-Based Resource Allocation Scheme for UAV-Assisted Internet of Remote Things Systems}, volume={11}, ISSN={["2169-3536"]}, DOI={10.1109/ACCESS.2023.3279401}, abstractNote={Multi-layered communication networks including satellites and unmanned aerial vehicles (UAVs) with remote sensing capability are expected to be an essential part of next-generation wireless communication systems. It has been reported that deep reinforcement learning algorithm brings performance improvement in various practical wireless communication environments. However, it is anticipated that the computational complexity will be a critical issue as the number of devices in the network is significantly increased. To resolve this problem, in this paper we propose a multi-agent reinforcement learning (MARL)-based resource allocation scheme for UAV-assisted Internet of remote things (IoRT) systems. The UAV and IoRT sensors are set to be MARL agents, which are independently trained to minimize energy consumption cost for communication by controlling the transmit power and bandwidth. It is shown that the UAV agent can reduce energy consumption by 70.9195 kJ, while the IoRT sensor agents yield 20.5756 kJ reduction, which are 65.4 % and 71.97 % reductions compared to the initial state of each agent. Moreover, the effects from the hyperparameters of the neural episodic control (NEC) baseline algorithm are investigated in terms of power consumption.}, journal={IEEE ACCESS}, author={Lee, Donggu and Sun, Young Ghyu and Kim, Soo Hyun and Kim, Jae-Hyun and Shin, Yoan and Kim, Dong In and Kim, Jin Young}, year={2023}, pages={53155–53164} }
 @article{lee_guvenc_2023, title={Rank and Condition Number Analysis for UAV MIMO Channels Using Ray Tracing}, ISSN={["2577-2465"]}, DOI={10.1109/VTC2023-Spring57618.2023.10199529}, abstractNote={Channel rank and condition number of multi-input multi-output (MIMO) channels can be effective indicators of achievable rates with spatial multiplexing in mobile networks. In this paper, we use extensive ray tracing simulations to investigate channel rank, condition number, and signal coverage distribution for air-to-ground MIMO channels. We consider UAV-based user equipment (UE) at altitudes of 3 m, 30 m, 70 m, and 110 m from the ground. Moreover, we also consider their communication link with a cellular base station in urban and rural areas. In particular, Centennial Campus and Lake Wheeler Road Field Labs of NC State University are considered, and their geographical information extracted from the open street map (OSM) database is incorporated into ray tracing simulations. Our results characterize how the channel rank tends to reduce as a function of UAV altitude, while also providing insights into the effects of geography, building distribution, and threshold parameters on channel rank and condition number.}, journal={2023 IEEE 97TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-SPRING}, author={Lee, Donggu and Guvenc, Ismail}, year={2023} }