1,725 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Meta-Reinforcement Learning for Timely and Energy-efficient Data Collection in Solar-powered UAV-assisted IoT Networks
Unmanned aerial vehicles (UAVs) have the potential to greatly aid Internet of
Things (IoT) networks in mission-critical data collection, thanks to their
flexibility and cost-effectiveness. However, challenges arise due to the UAV's
limited onboard energy and the unpredictable status updates from sensor nodes
(SNs), which impact the freshness of collected data. In this paper, we
investigate the energy-efficient and timely data collection in IoT networks
through the use of a solar-powered UAV. Each SN generates status updates at
stochastic intervals, while the UAV collects and subsequently transmits these
status updates to a central data center. Furthermore, the UAV harnesses solar
energy from the environment to maintain its energy level above a predetermined
threshold. To minimize both the average age of information (AoI) for SNs and
the energy consumption of the UAV, we jointly optimize the UAV trajectory, SN
scheduling, and offloading strategy. Then, we formulate this problem as a
Markov decision process (MDP) and propose a meta-reinforcement learning
algorithm to enhance the generalization capability. Specifically, the
compound-action deep reinforcement learning (CADRL) algorithm is proposed to
handle the discrete decisions related to SN scheduling and the UAV's offloading
policy, as well as the continuous control of UAV flight. Moreover, we
incorporate meta-learning into CADRL to improve the adaptability of the learned
policy to new tasks. To validate the effectiveness of our proposed algorithms,
we conduct extensive simulations and demonstrate their superiority over other
baseline algorithms
Optimization and Communication in UAV Networks
UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects
Ensemble DNN for Age-of-Information Minimization in UAV-assisted Networks
This paper addresses the problem of Age-of-Information (AoI) in UAV-assisted
networks. Our objective is to minimize the expected AoI across devices by
optimizing UAVs' stopping locations and device selection probabilities. To
tackle this problem, we first derive a closed-form expression of the expected
AoI that involves the probabilities of selection of devices. Then, we formulate
the problem as a non-convex minimization subject to quality of service
constraints. Since the problem is challenging to solve, we propose an Ensemble
Deep Neural Network (EDNN) based approach which takes advantage of the dual
formulation of the studied problem. Specifically, the Deep Neural Networks
(DNNs) in the ensemble are trained in an unsupervised manner using the
Lagrangian function of the studied problem. Our experiments show that the
proposed EDNN method outperforms traditional DNNs in reducing the expected AoI,
achieving a remarkable reduction of .Comment: 6 pages, 3 figure
A review of relay network on UAVS for enhanced connectivity
One of the best evolution in technology breakthroughs is the Unmanned Aerial Vehicle (UAV). This aerial system is able to perform the mission in an agile environment and can reach the hard areas to perform the tasks autonomously. UAVs can be used in post-disaster situations to estimate damages, to monitor and to respond to the victims. The Ground Control Station can also provide emergency messages and ad-hoc communication to the Mobile Users of the disaster-stricken community using this network. A wireless network can also extend its communication range using UAV as a relay. Major requirements from such networks are robustness, scalability, energy efficiency and reliability. In general, UAVs are easy to deploy, have Line of Sight options and are flexible in nature. However, their 3D mobility, energy constraints, and deployment environment introduce many challenges. This paper provides a discussion of basic UAV based multi-hop relay network architecture and analyses their benefits, applications, and tradeoffs. Key design considerations and challenges are investigated finding fundamental issues and potential research directions to exploit them. Finally, analytical tools and frameworks for performance optimizations are presented
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