Drone-Enabled Load Management for Solar Small Cell Networks in Next-Gen
Communications Optimization for Solar Small Cells
- URL: http://arxiv.org/abs/2311.02648v1
- Date: Sun, 5 Nov 2023 13:21:38 GMT
- Title: Drone-Enabled Load Management for Solar Small Cell Networks in Next-Gen
Communications Optimization for Solar Small Cells
- Authors: Daksh Dave, Dhruv Khut, Sahil Nawale, Pushkar Aggrawal, Disha Rastogi
and Kailas Devadkar
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: In recent years, the cellular industry has witnessed a major evolution in
communication technologies. It is evident that the Next Generation of cellular
networks(NGN) will play a pivotal role in the acceptance of emerging IoT
applications supporting high data rates, better Quality of Service(QoS), and
reduced latency. However, the deployment of NGN will introduce a power overhead
on the communication infrastructure. Addressing the critical energy constraints
in 5G and beyond, 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. This method effectively manages
energy deficit by transferring aerial BSs from high to low-energy cells,
depending on user density and the availability of aerial BSs, optimizing power
distribution in advanced cellular networks. The complexity of the proposed
system is significantly lower as compared to existing power cable transmission
systems currently employed in powering the BSs. Furthermore, our proposed
algorithm has been shown to reduce BS power outages while requiring a minimum
number of drone exchanges. We have conducted a thorough review on real-world
dataset to prove the efficacy of our proposed approach to support BS during
high load demand times
Related papers
- Enhancing Sum-Rate Performance in Constrained Multicell Networks: A Low-Information Exchange Approach [9.991446137941427]
We propose an innovative approach that dramatically reduces the need for information exchange between base stations to a mere few bits.
Our proposed method not only addresses the limitations imposed by current network infrastructure but also showcases significantly improved performance under constrained conditions.
arXiv Detail & Related papers (2024-04-03T05:34:32Z) - SkyCharge: Deploying Unmanned Aerial Vehicles for Dynamic Load
Optimization in Solar Small Cell 5G Networks [15.532817648696408]
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.
arXiv Detail & Related papers (2023-11-21T19:17:39Z) - 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) - 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) - Power Grid Congestion Management via Topology Optimization with
AlphaZero [0.27998963147546135]
We propose an AlphaZero-based grid topology optimization agent as a non-costly, carbon-free congestion management alternative.
Our approach ranked 1st in the WCCI 2022 Learning to Run a Power Network (L2RPN) competition.
arXiv Detail & Related papers (2022-11-10T14:39:28Z) - 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) - Integrating LEO Satellite and UAV Relaying via Reinforcement Learning
for Non-Terrestrial Networks [51.05735925326235]
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency.
We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation.
To maximize the end-to-end data rate, the satellite association and HAP location should be optimized.
We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
arXiv Detail & Related papers (2020-05-26T05:39:27Z) - Communication Efficient Federated Learning with Energy Awareness over
Wireless Networks [51.645564534597625]
In federated learning (FL), the parameter server and the mobile devices share the training parameters over wireless links.
We adopt the idea of SignSGD in which only the signs of the gradients are exchanged.
Two optimization problems are formulated and solved, which optimize the learning performance.
Considering that the data may be distributed across the mobile devices in a highly uneven fashion in FL, a sign-based algorithm is proposed.
arXiv Detail & Related papers (2020-04-15T21:25:13Z) - Deep Learning for Radio Resource Allocation with Diverse
Quality-of-Service Requirements in 5G [53.23237216769839]
We develop a deep learning framework to approximate the optimal resource allocation policy for base stations.
We find that a fully-connected neural network (NN) cannot fully guarantee the requirements due to the approximation errors and quantization errors of the numbers of subcarriers.
Considering that the distribution of wireless channels and the types of services in the wireless networks are non-stationary, we apply deep transfer learning to update NNs in non-stationary wireless networks.
arXiv Detail & Related papers (2020-03-29T04:48:22Z)
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.