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

• AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT [32.6091251316091]
  This paper proposes a UAV-assisted system based on distributed beamforming to enhance age forwarding information (AoI) in Internet of Things (IoT) We propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing stability and accelerate convergence.
  arXiv  Detail & Related papers  (2025-02-13T07:48:36Z)
• Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning [52.64813150003228]
  The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV.
  arXiv  Detail & Related papers  (2025-01-11T02:32:42Z)
• Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks [60.085771314013044]
  Low-altitude economy holds significant potential for development in areas such as communication and sensing. We propose a Clustering-based Multi-agent Deep Deterministic Policy Gradient (CMADDPG) algorithm to address the multi-UAV cooperative task scheduling challenges in SAGIN.
  arXiv  Detail & Related papers  (2024-12-14T06:17:33Z)
• DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach [16.139481340656552]
  This paper presents a joint approach that combines multiple-agent reinforcement learning (MARL) and generative diffusion models (GDM) In the second stage, we introduce a novel DNN task assignment algorithm, termed GDM-MADDPG, which utilizes the reverse denoising process of GDM to replace the actor network in multi-agent deep deterministic policy gradient (MADDPG) Simulation results indicate that our algorithm performs favorably compared to benchmarks in terms of path planning, Age of Information (AoI), energy consumption, and task load balancing.
  arXiv  Detail & Related papers  (2024-11-13T02:41:02Z)
• 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)
• 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)

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.