Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2003.07574v1
• Date: Tue, 17 Mar 2020 08:16:14 GMT
• Title: Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning
• Authors: Yong Zeng, Xiaoli Xu, Shi Jin, Rui Zhang
• Abstract summary: How to achieve ubiquitous 3D communication coverage for UAVs in the sky is a new challenge. We propose a new coverage-aware navigation approach, which exploits the UAV's controllable mobility to design its navigation/trajectory. We propose a new framework called simultaneous navigation and radio mapping (SNARM), where the UAV's signal measurement is used to train the deep Q network.
• Score: 46.55077580093577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to unlock the full potential of UAVs in the future. However, how to achieve ubiquitous three-dimensional (3D) communication coverage for the UAVs in the sky is a new challenge. In this paper, we tackle this challenge by a new coverage-aware navigation approach, which exploits the UAV's controllable mobility to design its navigation/trajectory to avoid the cellular BSs' coverage holes while accomplishing their missions. We formulate an UAV trajectory optimization problem to minimize the weighted sum of its mission completion time and expected communication outage duration, and propose a new solution approach based on the technique of deep reinforcement learning (DRL). To further improve the performance, we propose a new framework called simultaneous navigation and radio mapping (SNARM), where the UAV's signal measurement is used not only for training the deep Q network (DQN) directly, but also to create a radio map that is able to predict the outage probabilities at all locations in the area of interest. This thus enables the generation of simulated UAV trajectories and predicting their expected returns, which are then used to further train the DQN via Dyna technique, thus greatly improving the learning efficiency.

Related papers

• UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment [17.531224704021273]
  This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based on a deep reinforcement learning method. We propose a deep reinforcement learning (DRL)-based method combined with human-in-the-loop, which allows the UAV to avoid obstacles automatically during flying.
  arXiv  Detail & Related papers  (2023-04-07T01:44:05Z)
• Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach [93.67588414950656]
  In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a 3D environment. We present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.
  arXiv  Detail & Related papers  (2021-07-23T03:33:29Z)
• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
  We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
  arXiv  Detail & Related papers  (2021-07-19T12:54:04Z)
• 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)
• Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning [1.2330326247154968]
  Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks. In this paper, we aim to find collision-free paths for multiple cellular-connected UAVs. We propose an offline temporal difference (TD) learning algorithm with online signal-to-interference-plus-noise ratio mapping to solve the problem.
  arXiv  Detail & Related papers  (2021-04-09T16:52:33Z)
• Artificial Intelligence for UAV-enabled Wireless Networks: A Survey [72.10851256475742]
  Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Artificial intelligence (AI) is growing rapidly nowadays and has been very successful. We provide a comprehensive overview of some potential applications of AI in UAV-based networks.
  arXiv  Detail & Related papers  (2020-09-24T07:11:31Z)
• Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach [88.45509934702913]
  We design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed. We incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time.
  arXiv  Detail & Related papers  (2020-02-21T07:29:15Z)
• Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach [36.587096293618366]
  An emerging problem is to track unauthorized small unmanned aerial vehicles (UAVs) hiding behind buildings. This paper proposes the idea of a dynamic radar network of UAVs for real-time and high-accuracy tracking of malicious targets.
  arXiv  Detail & Related papers  (2020-01-13T23:23:09Z)

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.