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Creators/Authors contains: "Gursoy, M. Cenk"

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  1. In this paper, we consider multi-agent deep reinforcement learning (deep RL) based network slicing agents in a dynamic environment with multiple base stations and multiple users. We develop a deep RL based jammer with limited prior information and limited power budget. The goal of the jammer is to minimize the transmission rates achieved with network slicing and thus degrade the network slicing agents' performance. We design a jammer with both listening and jamming phases and address jamming location optimization as well as jamming channel optimization via deep RL. We evaluate the jammer at the optimized location, generating interference attacks in the optimized set of channels by switching between the jamming phase and listening phase. We show that the proposed jammer can significantly reduce the victims' performance without direct feedback or prior knowledge on the network slicing policies. 
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    Free, publicly-accessible full text available September 1, 2024
  2. In this paper, we investigate the reliability in an unmanned aerial vehicle (UAV) assisted caching-based downlink network where non-orthogonal multiple access (NOMA) transmission and finite blocklength (FBL) codes are adopted. In this network, the ground user equipments (GUEs) request contents from a distant base station (BS) but there are no direct links from the BS to the GUEs. A UAV with limited cache size is employed to assist the BS to complete the communication by either first requesting the uncached contents from the BS and then serving the GUEs or directly sending the cached contents to the GUEs. In this setting, we first introduce the decoding error rate in the FBL regime as well as the caching policy at the UAV, and subsequently we construct an optimization problem aiming to minimize the maximum end-to-end decoding error rate among all GUEs under both coding length and maximum UAV transmission power constraints. A two-step alternating algorithm is proposed to solve the problem and numerical results demonstrate that our algorithm can solve the optimization problem efficiently. More specifically, loosening the FBL constraint, enlarging the cache size and having a higher transmission power budget at the UAV lead to an improved performance. 
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    Free, publicly-accessible full text available July 1, 2024
  3. This paper considers the trajectory design problem for unmanned aerial vehicles (UAVs) via meta-reinforcement learning. It is assumed that the UAV can move in different directions to explore a specific area and collect data from the ground nodes (GNs) located in the area. The goal of the UAV is to reach the destination and maximize the total data collected during the flight on the trajectory while avoiding collisions with other UAVs. In the literature on UAV trajectory designs, vanilla learning algorithms are typically used to train a task-specific model, and provide near-optimal solutions for a specific spatial distribution of the GNs. However, this approach requires retraining from scratch when the locations of the GNs vary. In this work, we propose a meta reinforcement learning framework that incorporates the method of Model-Agnostic Meta-Learning (MAML). Instead of training task-specific models, we train a common initialization for different distributions of GNs and different channel conditions. From the initialization, only a few gradient descents are required for adapting to different tasks with different GN distributions and channel conditions. Additionally, we also explore when the proposed MAML framework is preferred and can outperform the compared algorithms. 
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    Free, publicly-accessible full text available May 20, 2024
  4. Free, publicly-accessible full text available January 1, 2024
  5. Free, publicly-accessible full text available January 1, 2024
  6. Command and control (C2) data links over cellular networks is envisioned to be a reliable communications modality for various types of missions for Unmanned Aircraft System (UAS). The planning of UAS traffic and the provision of cellular communication resources are cross-coupled decisions that should be analyzed together to understand the quality of service such a modality can provide that meets business needs. The key to effective planning is the accurate estimation of communication link quality and the resource usage for a given air traffic requirement. In this work, a simulation and modelling framework is developed that integrates two open-source simulation platforms, Repast Simphony and ns-3, to generate UAS missions over different geographical areas and simulates the provision of 4G/5G cellular network connectivity to support their C2 and mission data links. To the best of our knowledge, this is the first simulator that co-simulates air traffic and cellular network communications for UAS while leveraging standardized 3GPP propagation models and incorporating detailed management of communication channels (i.e., resource blocks) at the cellular base station level. Three experiments were executed to demonstrate how the integrated simulation platform can be used to provide guidelines in communication resource allocation, air traffic management, and mission safety management in beyond visual line of sight (BVLOS) operations. 
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