SkyCharge: Deploying Unmanned Aerial Vehicles for Dynamic Load Optimization in Solar Small Cell 5G Networks

• URL: http://arxiv.org/abs/2311.12944v3
• Date: Fri, 9 Feb 2024 06:42:41 GMT
• Title: SkyCharge: Deploying Unmanned Aerial Vehicles for Dynamic Load Optimization in Solar Small Cell 5G Networks
• Authors: Daksh Dave, Vinay Chamola, Sandeep Joshi, Sherali Zeadally
• Abstract summary: We propose a novel user load transfer approach using airborne base stations mounted on drones for reliable and secure power redistribution. Depending on the user density and the availability of an aerial BS, the energy requirement of a cell with an energy deficit is accommodated by migrating the aerial BS from a high-energy to a low-energy cell. The proposed algorithm reduces power outages at BSs and maintains consistent throughput stability, thereby demonstrating its capability to boost the reliability and robustness of wireless communication systems.
• Score: 15.532817648696408
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The power requirements posed by the fifth-generation and beyond cellular networks are an important constraint in network deployment and require energy-efficient solutions. In this work, we propose a novel user load transfer approach using airborne base stations (BS) mounted on drones for reliable and secure power redistribution across the micro-grid network comprising green small cell BSs. Depending on the user density and the availability of an aerial BS, the energy requirement of a cell with an energy deficit is accommodated by migrating the aerial BS from a high-energy to a low-energy cell. The proposed hybrid drone-based framework integrates long short-term memory with unique cost functions using an evolutionary neural network for drones and BSs and efficiently manages energy and load redistribution. The proposed algorithm reduces power outages at BSs and maintains consistent throughput stability, thereby demonstrating its capability to boost the reliability and robustness of wireless communication systems.

Related papers

• RL-Based Cargo-UAV Trajectory Planning and Cell Association for Minimum Handoffs, Disconnectivity, and Energy Consumption [23.734853912297634]
  Unmanned aerial vehicle (UAV) is a promising technology for last-mile cargo delivery. Existing cellular networks were primarily designed to service ground users. We propose a novel approach for joint cargo-UAV trajectory planning and cell association.
  arXiv  Detail & Related papers  (2023-12-05T04:06:09Z)
• Drone-Enabled Load Management for Solar Small Cell Networks in Next-Gen Communications Optimization for Solar Small Cells [0.0]
  This study introduces an innovative load transfer method using drone-carried airborne base stations (BSs) for stable and secure power reallocation within a green micro-grid network. The complexity of the proposed system is significantly lower as compared to existing power cable transmission systems. Our proposed algorithm has been shown to reduce BS power outages while requiring a minimum number of drone exchanges.
  arXiv  Detail & Related papers  (2023-11-05T13:21:38Z)
• Reducing the Environmental Impact of Wireless Communication via Probabilistic Machine Learning [2.0610589722626074]
  Communication related energy consumption is high and is expected to grow in future networks in spite of anticipated efficiency gains in 6G. We present summaries of two problems, from both current and next generation network specifications, where probabilistic inference methods were used to great effect. We are able to safely reduce the energy consumption of existing hardware on a live communications network by $11%$ whilst maintaining operator specified performance envelopes.
  arXiv  Detail & Related papers  (2023-09-19T09:48:40Z)
• 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)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications [64.1076645382049]
  Combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency. In this paper, we study the spectrum, energy, and time resource management for deterministic-CR-NOMA IoT systems.
  arXiv  Detail & Related papers  (2021-09-17T08:55:48Z)
• A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence [152.89360859658296]
  5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC) On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in 3D space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference.
  arXiv  Detail & Related papers  (2020-10-19T08:56:04Z)
• Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells [3.8779763612314624]
  Mobile network operators (MNOs) are deploying dense infrastructures of small cells. This increases the power consumption of mobile networks, thus impacting the environment. In this paper, we consider a network of ambient small cells powered by energy harvesters and equipped with rechargeable batteries.
  arXiv  Detail & Related papers  (2020-08-07T12:27:01Z)
• A Deep Reinforcement Learning Approach to Efficient Drone Mobility Support [9.408748328358264]
  Existing cellular networks targeting terrestrial usage can support the initial deployment of low-altitude drone users. We propose a novel handover framework for providing efficient mobility support and reliable wireless connectivity to drones served by a terrestrial cellular network.
  arXiv  Detail & Related papers  (2020-05-11T16:21:04Z)
• Wireless Power Control via Counterfactual Optimization of Graph Neural Networks [124.89036526192268]
  We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating over a single shared wireless medium. To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture. We then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions.
  arXiv  Detail & Related papers  (2020-02-17T07:54:39Z)

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.