Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks

Zhu, Ming; Liu, Xiaoyang; Wang, Xiaodong

doi:10.48550/arxiv.1906.05015

Cited by 5 publications

(5 citation statements)

References 35 publications

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“…In [155], to jointly design a 3D trajectory for a network of UAVs while at the same time maximizing its throughput, a markov decision process (MDP) problem was formulated. The authors used a deep deterministic policy gradient (DDPG) algorithm to deal with this problem.…”

Section: ) Joint Communication and Fight Control Aspectsmentioning

confidence: 99%

Self-Evolving Integrated Vertical Heterogeneous Networks

Farajzadeh

Khoshkholgh²,

Yanıkömeroğlu

et al. 2023

IEEE Open J. Commun. Soc.

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Section: ) Joint Communication and Fight Control Aspectsmentioning

confidence: 99%

Self-Evolving Integrated Vertical Heterogeneous Networks

Farajzadeh

Khoshkholgh²,

Yanıkömeroğlu

et al. 2023

IEEE Open J. Commun. Soc.

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“…In [110], to jointly design a 3D trajectory for a network of UAVs while at the same time maximizing its throughput, a markov decision process (MDP) problem was formulated. The authors used deep deterministic policy gradient (DDPG) algorithm to deal with this problem.…”

Section: ) Intelligent Trajectory and Placement Designmentioning

confidence: 99%

Self-Evolving Integrated Vertical Heterogeneous Networks

Farajzadeh¹,

Khoshkholgh²,

Yanıkömeroğlu³

et al. 2021

Preprint

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The evolution of future networks into fully autonomous entities appears inevitable. The need for greater operational agility in supporting a wide range of services and use cases together with increasing network complexity creates, in all likelihood, unforeseen new challenges. 6G and beyond networks will have a disruptive architecture imposing some crucial requirements:1) Support universal access to intelligence sustained by a distributed computing, sensing, and control infrastructure; 2) manage with a unified framework of a heterogeneous communication, computing, caching, and control infrastructure; 3) support highly dynamic and possibly intermittent networks of vertical networks.This study first advocates the merits of integrated vertical heterogeneous network (VHetNet) architecture in support of 6G infrastructure. We then review the current literature on network management of vertical networks and discuss a unified network management framework to analyze new architecture-dependent challenges and their coordination with existing network management services and requirements. Third, we investigate recent advancements in artificial intelligence (AI)/machine learning (ML) methods in current VHetNets and discuss the core challenges of integrating AI/ML in VHetNets. Finally, we discuss potential future research directions for advancing the autonomous and evolving capabilities of VHetNets.

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“…The aim in [15] was to maximize the number of serving users by proposing a double Q-learning based algorithm. The authors in [16] considered a vehicular control network and utilized an UAV to improve their network performance. They utilized a deep learning algorithm based on Deep Deterministic Policy Gradient (DDPG) for resource allocation and UAV trajectory design.…”

Section: A Motivationmentioning

confidence: 99%

“…UAV u sends signal to user k on subcarrier l through a channel with the channel power gain hl uk (t), which consists of line of sight (LoS) and non-line of sight (NLoS) links as follows [16]:…”

Section: A Communication Model 1) Uav To User Communication (Dl Acces...mentioning

confidence: 99%

AI-Based and Mobility-Aware Energy Efficient Resource Allocation and Trajectory Design for NFV Enabled Aerial Networks

Pourghasemian,

Abedi,

Salarhosseini

et al. 2021

Preprint

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In this paper, we propose a novel joint intelligent trajectory design and resource allocation algorithm based on user's mobility and their requested services for unmanned aerial vehicles (UAVs) assisted networks, where UAVs act as nodes of a network function virtualization (NFV) enabled network. Our objective is to maximize energy efficiency and minimize the average delay on all services by allocating the limited radio and NFV resources. In addition, due to the traffic conditions and mobility of users, we let some Virtual Network Functions (VNFs) to migrate from their current locations to other locations to satisfy the Quality of Service requirements. We formulate our problem to find near-optimal locations of UAVs, transmit power, subcarrier assignment, placement, and scheduling the requested service's functions over the UAVs and perform suitable VNF migration. Then we propose a novel Hierarchical Hybrid Continuous and Discrete Action (HHCDA) deep reinforcement learning method to solve our problem. Finally, the convergence and computational complexity of the proposed algorithm and its performance analyzed for different parameters. Simulation results show that our proposed HHCDA method decreases the request reject rate and average delay by 31.5% and 20% and increases the energy efficiency by 40% compared to DDPG method.

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Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks

Cited by 5 publications

References 35 publications

Self-Evolving Integrated Vertical Heterogeneous Networks

Self-Evolving Integrated Vertical Heterogeneous Networks

Self-Evolving Integrated Vertical Heterogeneous Networks

AI-Based and Mobility-Aware Energy Efficient Resource Allocation and Trajectory Design for NFV Enabled Aerial Networks

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