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1. Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach

   https://doi.org/10.1109/WCNC51071.2022.9771893  

   Abdalla, Aly Sabri; Behfarnia, Ali; Marojevic, Vuk (April 2022, 2022 IEEE Wireless Communications and Networking Conference (WCNC))

   The unmanned aerial vehicle (UAV) is one of the technological breakthroughs that supports a variety of services, including communications. UAVs can also enhance the security of wireless networks. This paper defines the problem of eavesdropping on the link between the ground user and the UAV, which serves as an aerial base station (ABS). The reinforcement learning algorithms Q-learning and deep Q-network (DQN) are proposed for optimizing the position of the ABS and the transmission power to enhance the data rate of the ground user. This increases the secrecy capacity without the system knowing the location of the eavesdropper. Simulation results show fast convergence and the highest secrecy capacity of the proposed DQN compared to Q-learning and two baseline approaches. 

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2. A Deep Reinforcement Learning Approach for Autonomous Reconfigurable Intelligent Surfaces

   https://doi.org/10.1109/ICCWorkshops59551.2024.10615955  

   Choi, Hyuckjin; Nguyen, Ly V; Choi, Junil; Swindlehurst, A Lee (June 2024, IEEE)

   A reconfigurable intelligent surface (RIS) is a prospective wireless technology that enhances wireless channel quality. An RIS is often equipped with passive array of elements and provides cost and power-efficient solutions for coverage extension of wireless communication systems. Without any radio frequency (RF) chains or computing resources, however, the RIS requires control information to be sent to it from an external unit, e.g., a base station (BS). The control information can be delivered by wired or wireless channels, and the BS must be aware of the RIS and the RIS-related channel conditions in order to effectively configure its behavior. Recent works have introduced hybrid RIS structures possessing a few active elements that can sense and digitally process received data. Here, we propose the operation of an entirely autonomous RIS that operates without a control link between the RIS and BS. Using a few sensing elements, the autonomous RIS employs a deep Q network (DQN) based on reinforcement learning in order to enhance the sum rate of the network. Our results illustrate the potential of deploying autonomous RISs in wireless networks with essentially no network overhead. 

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3. Joint Trajectory Design and Resource Optimization in UAV-Assisted Caching-Enabled Networks with Finite Blocklength Transmissions

   https://doi.org/10.3390/drones8010012  

   Yang, Yang; Gursoy, Mustafa (January 2024, Drones)

   In this study, we design and analyze a reliability-oriented downlink wireless network assisted by unmanned aerial vehicles (UAVs). This network employs non-orthogonal multiple access (NOMA) transmission and finite blocklength (FBL) codes. In the network, ground user equipments (GUEs) request content from a remote base station (BS), and there are no direct connections between the BS and the GUEs. To address this, we employ a UAV with a limited caching capacity to assist the BS in completing the communication. The UAV can either request uncached content from the BS and then serve the GUEs or directly transmit cached content to the GUEs. In this paper, we first introduce the decoding error rate within the FBL regime and explore caching policies for the UAV. Subsequently, we formulate an optimization problem aimed at minimizing the average maximum end-to-end decoding error rate across all GUEs while considering the coding length and maximum UAV transmission power constraints. We propose a two-step alternating optimization scheme embedded within a deep deterministic policy gradient (DDPG) algorithm to jointly determine the UAV trajectory and transmission power allocations, as well as blocklength of downloading phase, and our numerical results show that the combined learning-optimization algorithm efficiently addresses the considered problem. In particular, it is shown that a well-designed UAV trajectory, relaxing the FBL constraint, increasing the cache size, and providing a higher UAV transmission power budget all lead to improved performance. 

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4. Distributed Data-Driven Learning-Based Optimal Dynamic Resource Allocation for Multi-RIS-Assisted Multi-User Ad-Hoc Network

   https://doi.org/10.3390/a17010045  

   Zhang, Yuzhu; Xu, Hao (January 2024, Algorithms)

   This study investigates the problem of decentralized dynamic resource allocation optimization for ad-hoc network communication with the support of reconfigurable intelligent surfaces (RIS), leveraging a reinforcement learning framework. In the present context of cellular networks, device-to-device (D2D) communication stands out as a promising technique to enhance the spectrum efficiency. Simultaneously, RIS have gained considerable attention due to their ability to enhance the quality of dynamic wireless networks by maximizing the spectrum efficiency without increasing the power consumption. However, prevalent centralized D2D transmission schemes require global information, leading to a significant signaling overhead. Conversely, existing distributed schemes, while avoiding the need for global information, often demand frequent information exchange among D2D users, falling short of achieving global optimization. This paper introduces a framework comprising an outer loop and inner loop. In the outer loop, decentralized dynamic resource allocation optimization has been developed for self-organizing network communication aided by RIS. This is accomplished through the application of a multi-player multi-armed bandit approach, completing strategies for RIS and resource block selection. Notably, these strategies operate without requiring signal interaction during execution. Meanwhile, in the inner loop, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm has been adopted for cooperative learning with neural networks (NNs) to obtain optimal transmit power control and RIS phase shift control for multiple users, with a specified RIS and resource block selection policy from the outer loop. Through the utilization of optimization theory, distributed optimal resource allocation can be attained as the outer and inner reinforcement learning algorithms converge over time. Finally, a series of numerical simulations are presented to validate and illustrate the effectiveness of the proposed scheme. 

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5. Deep Reinforcement Learning for Secure MEC Service in Vehicular Networks with Reconfigurable Intelligent Surfaces

   https://doi.org/10.1109/IEEECONF59524.2023.10476897  

   Wang, Haoyu; Bai, Haowen; Ju, Ying; Swindlehurst, A Lee (October 2023, Proc. 57th Asilomar Conference on Signals, Systems, and Computers)

   The broadcasting nature of wireless signals may result in the task offloading process of mobile edge computing (MEC) suffering serious information leakage. As a novel technology, physical layer security (PLS) combined with reconfigurable intelligent surfaces (RIS) can enhance transmission quality and security. This paper investigates the MEC service delay problem in RIS-aided vehicular networks under malicious eavesdropping. Due to the lack of an explicit formulation for the optimization problem, we propose a deep deterministic policy gradient (DDPG)-based communication scheme to optimize the secure MEC service. It aims to minimize the maximum MEC service time while reducing eavesdropping threats by jointly designing the RIS phase shift matrix and computing resource allocation in real-time. Simulation results demonstrate that 1) the DDPG-based scheme can help the base station make reasonable actions to realize secure MEC service in dynamic MEC vehicular networks; 2) deploying RIS can dramatically reduce eavesdropping threats and improve the overall MEC service quality. 

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