Joint UAV Placement Optimization, Resource Allocation, and Computation Offloading for THz Band: A DRL Approach

Wang, Heng; Zhang, Haijun; Liu, Xiangnan; Long, Keping; Nallanathan, Arumugam

doi:10.1109/twc.2022.3230407

Cited by 29 publications

(5 citation statements)

References 32 publications

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“…• Optimization of Single UAV (OSU) [39]: This solution studies the task offloading problem in a single UAV scenario, which takes the energy consumption as a constraint and task delay as the optimization objective. It employs an algorithm based on deep deterministic policy gradient (DDPG) to search for near-optimal solutions in highly dynamic environments.…”

Section: B Alternative Solutionsmentioning

confidence: 99%

Joint Task Offloading, Resource Allocation, and Trajectory Design for Multi-UAV Cooperative Edge Computing With Task Priority

Hao,

Xu,

Zhang

et al. 2024

IEEE Trans. on Mobile Comput.

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Mobile edge computing (MEC) has emerged as a solution to address the demands of computation-intensive network services by providing computational capabilities at the network edge, thus reducing service delay. Due to the flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed to assist MEC. This paper investigates the task offloading problem in a UAV-assisted MEC system with collaboration of multiple UAVs, highlighting task priorities and binary offloading mode. We defined the system gain based on energy consumption and task delay. The joint optimization of UAVs' trajectory design, binary offloading decision, computation resources allocation, and communication resources management is formulated as a mixed integer programming problem with the goal of maximizing the long-term average system gain. Considering the discretecontinuous hybrid action space of this problem, we propose a novel deep reinforcement learning (DRL) algorithm based on the latent space to solve it. The evaluation results demonstrate that our proposed algorithm outperforms four state-of-the-art alternative solutions in terms of task delay and system gain.

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Section: B Alternative Solutionsmentioning

confidence: 99%

Joint Task Offloading, Resource Allocation, and Trajectory Design for Multi-UAV Cooperative Edge Computing With Task Priority

Hao,

Xu,

Zhang

et al. 2024

IEEE Trans. on Mobile Comput.

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“…Table 5 illustrates RL methods applied to improve the THz technology with their potential and shortcomings. The highlighted approaches in Table 5 are designed to improve the propagation attenuation [132], interference mitigation [133], multi-hop communication [134], THz beamforming [135], resource allocation to D2D-enabled wireless networks [136], MAC protocol for LoS mobile networks [99], latency minimization [137] etc.…”

Section: Reinforcement Learning: State Of the Artmentioning

confidence: 99%

“…A DRL algorithm for joint optimization of UAV placement, resource allocation, and computation offloading is investigated [137]. The proposed deep Q-learning (DQN) and DDPG search for near-optimal solutions were studied in a highly dynamic environment.…”

Section: Reinforcement Learning: State Of the Artmentioning

confidence: 99%

Terahertz Meets AI: The State of the Art

Farhad

Pyun

2023

Sensors

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Terahertz (THz) is a promising technology for future wireless communication networks, particularly for 6G and beyond. The ultra-wide THz band, ranging from 0.1 to 10 THz, can potentially address the limited capacity and scarcity of spectrum in current wireless systems such as 4G-LTE and 5G. Furthermore, it is expected to support advanced wireless applications requiring high data transmission and quality services, i.e., terabit-per-second backhaul systems, ultra-high-definition streaming, virtual/augmented reality, and high-bandwidth wireless communications. In recent years, artificial intelligence (AI) has been used mainly for resource management, spectrum allocation, modulation and bandwidth classification, interference mitigation, beamforming, and medium access control layer protocols to improve THz performance. This survey paper examines the use of AI in state-of-the-art THz communications, discussing the challenges, potentials, and shortcomings. Additionally, this survey discusses the available platforms, including commercial, testbeds, and publicly available simulators for THz communications. Finally, this survey provides future strategies for improving the existing THz simulators and using AI methods, including deep learning, federated learning, and reinforcement learning, to improve THz communications.

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“…The advent of 5G and beyond networks has been made possible by advancements in networking technologies and novel computing-communication frameworks (Liu et al, 2017). Among these technologies, Mobile Edge Computing (MEC) is crucial in enhancing network performance by facilitating distributed computation (Huda & Moh, 2022;Wang, Zhang, Liu, Long, & Nallanathan, 2022). MEC brings computational power and energy resources to the network edge, allowing decentralized computation capabilities.…”

Section: Introductionmentioning

confidence: 99%

“…By augmenting MEC servers with extra computational power, UAV-aided MEC expedites computations and enhances the battery life of mobile devices. In addition, artificial intelligence, particularly Deep Reinforcement Learning (DRL), is utilized in conjunction with MEC to enable intelligent decision-making (Cheng et al, 2019;Wang et al, 2022). A notable algorithm utilized in this context is the deep Q-network (DQN) (Mnih et al, 2015), which integrates Deep Neural Networks (DNNs) with reinforcement learning to autonomously tackle complex decision-making issues.…”

Section: Introductionmentioning

confidence: 99%

Latency-aware task offloading in UAV-assisted edge computing: Leveraging deep reinforcement learning

Hai

2023

HCMCOUJS - ENGINEERING AND TECHNOLOGY

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Due to the exponential expansion of the Internet of Things (IoT) technology, the increasing amount of data generated by numerous IoT devices has become a significant challenge for communication and computing networks. On the other hand, the concept of Mobile Edge Computing (MEC) presents a viable solution as it brings computing, storage, and networking capabilities in close proximity to the user, enabling the hosting of applications that require significant computation power and low latency right at the network’s edge. In addition, the maneuverability, cost-effectiveness, and ease of deployment make Unmanned Aerial Vehicles (UAVs) highly versatile wireless platforms. Capitalizing on the benefits of MEC and UAVs, this paper proposes a UAV-assisted MEC system that offloads services to reduce the computation burden in IoT. The objective is to maximize the task completion rate, considering factors such as user transmit power, task offloading rate, and UAV trajectory variables. To tackle this optimization problem, this paper devises a method based on the Deep Deterministic Policy Gradient (DDPG), an algorithm for continuous action spaces in deep reinforcement learning. The numerical simulations show the effectiveness of the proposed approach in comparison to other benchmarks, i.e., deep Q-network (DQN) and full offloading methods. In a simulation, the proposed DDPG method can achieve a fully optimized system with a 100% task success rate, while the DQN-based method achieves a lower task success rate of 95.93% and the full offloading method only obtains a task success rate of 82.73%.

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Joint UAV Placement Optimization, Resource Allocation, and Computation Offloading for THz Band: A DRL Approach

Cited by 29 publications

References 32 publications

Joint Task Offloading, Resource Allocation, and Trajectory Design for Multi-UAV Cooperative Edge Computing With Task Priority

Joint Task Offloading, Resource Allocation, and Trajectory Design for Multi-UAV Cooperative Edge Computing With Task Priority

Terahertz Meets AI: The State of the Art

Latency-aware task offloading in UAV-assisted edge computing: Leveraging deep reinforcement learning

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