Resource Allocation for Multi-UAV Assisted IoT Networks: A Deep Reinforcement Learning Approach

Munaye, Yirga Yayeh; Juang, Rong‐Terng; Lin, Hsin‐Piao; Tarekegn, Getaneh Berie

doi:10.1109/icpai51961.2020.00011

Cited by 7 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A novel DQN-based method was introduced to address the complex problem. The authors in [25] analyzed RA for bandwidth, throughput, and power consumption in different scenarios for multi-UAV-assisted IoT networks. On the basis of machine learning, authors considered DRL to address the joint RA problem.…”

Section: Related Workmentioning

confidence: 99%

Double Deep Q-Network Method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

Ouamri¹,

Alkanhel²,

Singh³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation (5G) networks. However, it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand. To meet these access conditions and improve Quality of Service, resource allocation (RA) should be carefully optimized. Traditionally, RA problems are nonconvex optimizations, which are performed using heuristic methods, such as genetic algorithm, particle swarm optimization, and simulated annealing. However, the application of these approaches remains computationally expensive and unattractive for dense cellular networks. Therefore, artificial intelligence algorithms are used to improve traditional RA mechanisms. Deep learning is a promising tool for addressing resource management problems in wireless communication. In this study, we investigate a double deep Q-network-based RA framework that maximizes energy efficiency (EE) and total network throughput in unmanned aerial vehicle (UAV)-assisted terrestrial networks. Specifically, the system is studied under the constraints of interference. However, the optimization problem is formulated as a mixed integer nonlinear program. Within this framework, we evaluated the effect of height and the number of UAVs on EE and throughput. Then, in accordance with the experimental results, we compare the proposed algorithm with several artificial intelligence methods. Simulation results indicate that the proposed approach can increase EE with a considerable throughput.

show abstract

Section: Related Workmentioning

confidence: 99%

Double Deep Q-Network Method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

Ouamri¹,

Alkanhel²,

Singh³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…For the scenario of multi-drone assisted mobile edge computing, Abegaz et al [10] proposed a collaborative computation offloading and resource allocation (CCORA-DRL) scheme that uses DDPG techniques to optimize the resource allocation strategy that integrates time-varying compute node capacity, task and channel quality to minimize computational cost (energy consumption and latency) and resource allocation (power and computational resources). Yirga et al [11] simulated a scenario of allocating available resources to 35 ground users via 2 UAVs, used the MARL method for allocating bandwidth, throughput, and power consumption, and performed data collection and testing in a real-world environment to verify the fast convergence and accuracy of the algorithm. In the scenario of collecting data from multiple ground sensor nodes through a UAV network, Shi et al [12] proposed an attention mechanism-based DRL method to optimize the path planning of a UAV network with unequal energy based on the Age of Information (AAI).…”

Section: Drl-based Routing For Unmanned Cluster 31 Drl-based Routing ...mentioning

confidence: 99%

Review of unmanned cluster routing protocols based on deep reinforcement learning

Peng

Yuan

Liu

et al. 2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

View full text Add to dashboard Cite

Most unmanned clusters are typical mobile self-organizing networks (MANETs) with high dynamics and energy constraints. Deep reinforcement learning (DRL) is an emerging hotspot in the field of machine learning because it can make decisions without relying on models and is very suitable for dynamic unmanned cluster communication systems with time-varying network conditions. DRL -based unmanned cluster routing protocols are of great research value and significance. This paper introduces the fundamental characteristics of unmanned clusters and dynamic routing protocols; briefly describes the fundamental elements of DRL algorithms and their applicability in dynamic routing protocols; reviews the applications of DRL algorithms in unmanned clusters routing protocols; and summarizes the challenges and future development directions of unmanned clusters routing protocols based on DRL.

show abstract

“…where i = 1 is the LoS link between UAV and k th UE and i = 2 is the link between UAV and k th UE through RIS, and σ 2 n is the white noise power. Communication using (1) and assuming a Gaussian channel, the maximum achievable rate for the channel between BS to UAV, UAV to UE with and without RIS is given by the Shannon bound…”

Section: Scenario Definition and Problem Formulationmentioning

confidence: 99%

“…Increasing demand for sustainable and flexible connectivity specifically for either semi-urban/rural areas [1], [2] or disaster scenarios for monitoring and surveillance [3], [4], has led to focus on the usage of Unmanned Aerial Vehicles (UAVs) and Reconfigurable Intelligent Surfaces (RIS) for enhancing the network coverage and, thereby, the service availability of cellular networks. The conceptual design of RIS consists of several reflective elements which can be configured so as to reflect and, in particular, beamform a signal towards a particular direction.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Design for RIS-assisted UAV communications in beyond-5G Networks

Deshpande¹,

Vaca-Rubio²,

Mohebi³

et al. 2022

2022 20th Mediterranean Communication and Computer Networking Conference (MedComNet)

View full text Add to dashboard Cite

The usage of Reconfigurable Intelligent Surfaces (RIS) in conjunction with Unmanned Ariel Vehicles (UAVs) is being investigated as a way to provide energy-efficient communication to ground users in dense urban areas. In this paper, we devise an optimization scenario to reduce overall energy consumption in the network while guaranteeing certain Quality of Service (QoS) to the ground users in the area. Due to the complex nature of the optimization problem, we provide a joint UAV trajectory and RIS phase decision to minimize transmission power of the UAV and Base Station (BS) that yields good performance with lower complexity. So, the proposed method uses a Successive Convex Approximation (SCA) to iteratively determine a joint optimal solution for UAV Trajectory, RIS phase and BS and UAV Transmission Power. The simulation results show the algorithm provides a minimum guaranteed rate while minimising transmission power of UAV and BS.

show abstract

Resource Allocation for Multi-UAV Assisted IoT Networks: A Deep Reinforcement Learning Approach

Cited by 7 publications

References 20 publications

Double Deep Q-Network Method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

Double Deep Q-Network Method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

Review of unmanned cluster routing protocols based on deep reinforcement learning

Energy-Efficient Design for RIS-assisted UAV communications in beyond-5G Networks

Contact Info

Product

Resources

About