AI-based Radio and Computing Resource Allocation and Path Planning in NOMA NTNs: AoI Minimization under CSI Uncertainty

• URL: http://arxiv.org/abs/2305.00780v2
• Date: Mon, 29 May 2023 18:55:11 GMT
• Title: AI-based Radio and Computing Resource Allocation and Path Planning in NOMA NTNs: AoI Minimization under CSI Uncertainty
• Authors: Maryam Ansarifard, Nader Mokari, Mohammadreza Javan, Hamid Saeedi, Eduard A. Jorswieck
• Abstract summary: We develop a hierarchical aerial computing framework composed of high altitude platform (HAP) and unmanned aerial vehicles (UAVs) It is shown that task scheduling significantly reduces the average AoI. It is shown that power allocation has a marginal effect on the average AoI compared to using full transmission power for all users.
• Score: 23.29963717212139
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we develop a hierarchical aerial computing framework composed of high altitude platform (HAP) and unmanned aerial vehicles (UAVs) to compute the fully offloaded tasks of terrestrial mobile users which are connected through an uplink non-orthogonal multiple access (UL-NOMA). To better assess the freshness of information in computation-intensive applications the criterion of age of information (AoI) is considered. In particular, the problem is formulated to minimize the average AoI of users with elastic tasks, by adjusting UAVs trajectory and resource allocation on both UAVs and HAP, which is restricted by the channel state information (CSI) uncertainty and multiple resource constraints of UAVs and HAP. In order to solve this non-convex optimization problem, two methods of multi-agent deep deterministic policy gradient (MADDPG) and federated reinforcement learning (FRL) are proposed to design the UAVs trajectory, and obtain channel, power, and CPU allocations. It is shown that task scheduling significantly reduces the average AoI. This improvement is more pronounced for larger task sizes. On one hand, it is shown that power allocation has a marginal effect on the average AoI compared to using full transmission power for all users. Compared with traditional transmission schemes, the simulation results show our scheduling scheme results in a substantial improvement in average AoI.

Related papers

• GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network [14.857267338331708]
  This paper proposes the Qedgix framework, which combines graph neural networks (GNNs) and the QMIX algorithm to achieve distributed optimization of the Age of Information (AoI) for users in unknown scenarios. Simulation results demonstrate that the proposed algorithm significantly improves convergence speed while reducing the mean AoI values of users.
  arXiv  Detail & Related papers  (2024-08-18T02:29:10Z)
• UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
  We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs. We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
  arXiv  Detail & Related papers  (2024-04-11T03:19:22Z)
• Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs [21.195346908715972]
  Unmanned aerial vehicles present an alternative means to offload data traffic from terrestrial BSs. This paper presents a novel approach to efficiently serve multiple UAVs for data offloading from terrestrial BSs.
  arXiv  Detail & Related papers  (2024-02-05T12:36:08Z)
• Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications [80.35959154762381]
  Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications are expected to alleviate the load of ground base stations in a cost-effective way. Existing studies mainly focus on the deployment and resource allocation of a single IRS instead of multiple IRSs. We propose a new optimization algorithm for joint IRS-user association, trajectory optimization of UAVs, successive interference cancellation (SIC) decoding order scheduling and power allocation.
  arXiv  Detail & Related papers  (2023-12-08T01:57:10Z)
• Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays [55.736718475856726]
  Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT) Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points. We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
  arXiv  Detail & Related papers  (2023-08-03T02:49:50Z)
• Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks [4.042622147977782]
  We focus on the case where the collected data is time-sensitive, and it is critical to maintain its timeliness. Our objective is to optimally design the UAVs' trajectories and the subsets of visited IoT devices such as the global Age-of-Updates (AoU) is minimized.
  arXiv  Detail & Related papers  (2023-03-15T15:03:09Z)
• Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach [67.06539298956854]
  fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs)
  arXiv  Detail & Related papers  (2022-06-13T02:19:20Z)
• Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement Learning Approach [56.84948632954274]
  This paper investigates a master unmanned aerial vehicle (MUAV)-powered Internet of Things (IoT) network. We propose using a rechargeable auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to enhance the communication signals from the MUAV. Under the proposed model, we investigate the optimal collaboration strategy of these energy-limited UAVs to maximize the accumulated throughput of the IoT network.
  arXiv  Detail & Related papers  (2021-12-20T15:45:28Z)
• 3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning [75.78929539923749]
  Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. It is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT) In this paper, we design a new UAV-assisted IoT systems relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices.
  arXiv  Detail & Related papers  (2021-06-06T14:08:41Z)
• Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization [43.9909417652678]
  Unmanned Aerial Vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones. Thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications. Deep Neural Networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed on-board.
  arXiv  Detail & Related papers  (2021-05-23T20:19:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.