Towards Autonomous Satellite Communications: An AI-based Framework to Address System-level Challenges

• URL: http://arxiv.org/abs/2112.06055v1
• Date: Sat, 11 Dec 2021 19:36:58 GMT
• Title: Towards Autonomous Satellite Communications: An AI-based Framework to Address System-level Challenges
• Authors: Juan Jose Garau-Luis and Skylar Eiskowitz and Nils Pachler and Edward Crawley and Bruce Cameron
• Abstract summary: The next generation of satellite constellations is designed to better address the future needs of our connected society. There is still not a clear path to achieve fully-autonomous satellite systems. In this paper we try to bridge this gap by characterizing the system-level needs that must be met to increase satellite autonomy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The next generation of satellite constellations is designed to better address the future needs of our connected society: highly-variable data demand, mobile connectivity, and reaching more under-served regions. Artificial Intelligence (AI) and learning-based methods are expected to become key players in the industry, given the poor scalability and slow reaction time of current resource allocation mechanisms. While AI frameworks have been validated for isolated communication tasks or subproblems, there is still not a clear path to achieve fully-autonomous satellite systems. Part of this issue results from the focus on subproblems when designing models, instead of the necessary system-level perspective. In this paper we try to bridge this gap by characterizing the system-level needs that must be met to increase satellite autonomy, and introduce three AI-based components (Demand Estimator, Offline Planner, and Real Time Engine) that jointly address them. We first do a broad literature review on the different subproblems and identify the missing links to the system-level goals. In response to these gaps, we outline the three necessary components and highlight their interactions. We also discuss how current models can be incorporated into the framework and possible directions of future work.

Related papers

• Towards Neural Architecture Search for Transfer Learning in 6G Networks [4.863212763542215]
  We describe and review the state-of-the-art in Neural Architecture Search and Transfer Learning and their applicability in networking. We identify open research challenges and set directions with a specific focus on three main requirements with elements unique to the future network.
  arXiv  Detail & Related papers  (2024-06-04T14:01:03Z)
• Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems [62.252355444948904]
  This paper presents the findings of a literature review on the integration of edge intelligence (EI) and socialized learning (SL) SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents. We elaborate on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses.
  arXiv  Detail & Related papers  (2024-04-20T11:07:29Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• CSM-H-R: A Context Modeling Framework in Supporting Reasoning Automation for Interoperable Intelligent Systems and Privacy Protection [0.07499722271664144]
  We propose a novel framework for automation of High Level Context (HLC) reasoning across intelligent systems at scale. The design of the framework supports the sharing and inter context among intelligent systems and the components for handling CSMs and the management of hierarchy, relationship, and transition. The implementation of the framework experiments on the HLC reasoning into vector and matrix computing and presents the potential to reach next level of automation.
  arXiv  Detail & Related papers  (2023-08-21T22:21:15Z)
• Machine Learning-Based User Scheduling in Integrated Satellite-HAPS-Ground Networks [82.58968700765783]
  Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G) This paper showcases the prospects of machine learning in the context of user scheduling in integrated space-air-ground communications.
  arXiv  Detail & Related papers  (2022-05-27T13:09:29Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence [45.076180487387575]
  Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems.
  arXiv  Detail & Related papers  (2021-05-04T23:42:06Z)
• Enabling Cross-Domain Communication: How to Bridge the Gap between AI and HW Engineers [0.17205106391379021]
  A key issue in system design is the lack of communication between hardware, software and domain expert. Recent research work shows progress in automatic HW/SW co-design flows of neural accelerators. This position paper discusses possibilities how to establish such a methodology for systems that include (reconfigurable) dedicated accelerators.
  arXiv  Detail & Related papers  (2021-04-08T14:05:15Z)
• Artificial Intelligence for Satellite Communication: A Review [91.3755431537592]
  This work provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms. The application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing.
  arXiv  Detail & Related papers  (2021-01-25T13:01:16Z)
• Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
  Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components. There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed. We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
  arXiv  Detail & Related papers  (2020-07-13T20:30:26Z)
• Communication-Efficient Edge AI: Algorithms and Systems [39.28788394839187]
  Wide scale deployment of edge devices (e.g., IoT devices) generates an unprecedented scale of data. Such enormous data cannot all be sent from end devices to the cloud for processing. By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative.
  arXiv  Detail & Related papers  (2020-02-22T09:27:55Z)

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.