Proceedings Article10.1109/tsipn.2022.3150911
A Multi-Agent Collaborative Environment Learning Method for UAV Deployment and Resource Allocation
Zhaojun Dai,Yan Zhang,Wancheng Zhang,Xinran Luo,Zunwen He +4 more
- 01 Jan 2022
Vol. 8, pp 120-130
47
TL;DR: Simulation results indicate that the proposed resource allocation algorithm for the UAV networks based on multi-agent collaborative environment learning is able to effectively improve the network utility compared with the multi- agent deep reinforcement learning algorithm without information interaction.
read more
Abstract: The dynamic position deployment and resource allocation of the unmanned aerial vehicle (UAV) communication networks has great significance in terms of interference management, coverage enhancement, and capacity improvement. Since the transmission power and energy resources of the UAVs are limited and the actual communication environment is complex and time-varying, it is challenging for the multiple UAVs to dynamically make decisions to ensure the communication performance of the system. Meanwhile, the centralized architecture may generate a certain degree of communication delay and affect communication efficiency. Facing this challenge, a resource allocation algorithm for the UAV networks based on multi-agent collaborative environment learning is proposed. This method is based on a distributed architecture. Each UAV is modeled as an independent agent, which improves the utility of the UAV networks through the dynamic selection decisions of its deployment position, transmission power, and occupied sub-channels. Each UAV learns the mapping of the network information to the position deployment and resource selection decisions based on the reinforcement learning algorithm according to partial of the state information it can observe. For the overall network, a multi-agent reinforcement learning method based on federated learning is designed on the purpose of realizing information interaction and combined dispatching of the UAVs. In the multi-agent system, the framework of federated learning is introduced to realize the sharing of non-privacy data among the UAVs. Simulation results indicate that the proposed method can effectively improve the network utility compared with the multi-agent deep reinforcement learning algorithm without information interaction.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms
TL;DR: This paper aggregates and analyzes research pertaining to UAV swarms from databases such as Google Scholar, ScienceDirect, Scopus, IEEE Xplorer, and Wiley over the past decade to bolster swarm performance, scalability, and adoption rates in unmanned farming settings.
23
A Survey on Integrated Sensing, Communication, and Computing Networks for Smart Oceans
Minghui Dai,Yang Li,Pei Ying Li,Yuan Wu,Li Ping Qian,Bin Lin,Zhou Su +6 more
TL;DR: A comprehensive survey on the integrated sensing, communication, and computing networks (ISCCNs) for smart oceans based on the collaboration of space–air–ground–sea networks from four domains and five aspects is conducted.
Live Traffic Video Multicasting Services in UAVs-assisted Intelligent Transport Systems: A Multi-Actor Attention Critic Approach
TL;DR: In this paper , the authors proposed a traffic video multicasting scheme by using video splitting and group splitting techniques for unmanned aerial vehicles (UAVs) assisted intelligent transport systems (ITS), in which UAVs are considered as the eyes in the sky to capture real-time traffic videos.
15
An Improved Particle Swarm Optimization Algorithm for UAV Base Station Placement
Faezeh Pasandideh,Fabricio E. Rodriguez Cesen,Pedro Henrique Morgan Pereira,C. Esteve Rothenberg,Edison Pignaton de Freitas +4 more
TL;DR: The acquired results show that the proposed solution based on the integration of PSO and K-means algorithms provides a low packet loss and latency and indicates that most of the users in the considered scenarios are covered by the DBSs.
14
Content-Aware Transmission in UAV-Assisted Multicast Communication
TL;DR: In this paper , two content-sharing (CS) data transmission schemes were proposed to improve the average data rate of ground users in air-to-ground (A2G) communication links.
References
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Reinforcement learning: a survey
TL;DR: Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Optimal LAP Altitude for Maximum Coverage
TL;DR: An analytical approach to optimizing the altitude of LAPs to provide maximum radio coverage on the ground shows that the optimal altitude is a function of the maximum allowed pathloss and of the statistical parameters of the urban environment, as defined by the International Telecommunication Union.
A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks
TL;DR: In this paper, a joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm.
1.2K
3-D Placement of an Unmanned Aerial Vehicle Base Station (UAV-BS) for Energy-Efficient Maximal Coverage
TL;DR: This letter proposes an optimal placement algorithm for UAV-BSs that maximizes the number of covered users using the minimum transmit power and decouple the Uav-BS deployment problem in the vertical and horizontal dimensions without any loss of optimality.
871