3,177 research outputs found
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Leveraging Intelligent Computation Offloading with Fog/Edge Computing for Tactile Internet: Advantages and Limitations
[EN] With the recent advancement in wireless communication and networks, we are at the doorstep of the Tactile Internet. The Tactile Internet aims to enable the skill delivery and thereafter democratize the specialized skills for many emerging applications (e.g., remote medical, industrial machinery, remote robotics, autonomous driving). In this article, we start with the motivation of applying intelligent edge computing for computation offloading in the Tactile Internet. Afterward, we outline the main research challenges to leverage edge intelligence at the master, network, and controlled domain of the Tactile Internet. The key research challenges in the Tactile Internet lie in its stringent requirements such as ultra-low latency, ultra-high reliability, and almost zero service outage. We also discuss major entities in intelligent edge computing and their role in the Tactile Internet. Finally, several potential research challenges in edge intelligence for the Tactile Internet are highlighted.This work was supported in part by the National Natural Science Foundation of China under Grant 61901128, and Agile Edge Intelligence for Delay Sensitive IoT (AgilE-IoT) project (Grant No. 9131-00119B) of Independent Research Fund Denmark (DFF).Mukherjee, M.; Guo, M.; Lloret, J.; Zhang, Q. (2020). Leveraging Intelligent Computation Offloading with Fog/Edge Computing for Tactile Internet: Advantages and Limitations. IEEE Network. 34(5):322-329. https://doi.org/10.1109/MNET.001.200000432232934
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications
This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft SystemsUnmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.info:eu-repo/semantics/publishedVersio
A Hybrid Framework of Reinforcement Learning and Convex Optimization for UAV-Based Autonomous Metaverse Data Collection
Unmanned aerial vehicles (UAVs) are promising for providing communication
services due to their advantages in cost and mobility, especially in the
context of the emerging Metaverse and Internet of Things (IoT). This paper
considers a UAV-assisted Metaverse network, in which UAVs extend the coverage
of the base station (BS) to collect the Metaverse data generated at roadside
units (RSUs). Specifically, to improve the data collection efficiency, resource
allocation and trajectory control are integrated into the system model. The
time-dependent nature of the optimization problem makes it non-trivial to be
solved by traditional convex optimization methods. Based on the proposed
UAV-assisted Metaverse network system model, we design a hybrid framework with
reinforcement learning and convex optimization to {cooperatively} solve the
time-sequential optimization problem. Simulation results show that the proposed
framework is able to reduce the mission completion time with a given
transmission power resource.Comment: This paper appears in IEEE Network magazin
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