5,076 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
Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications
In this paper, the efficient deployment and mobility of multiple unmanned
aerial vehicles (UAVs), used as aerial base stations to collect data from
ground Internet of Things (IoT) devices, is investigated. In particular, to
enable reliable uplink communications for IoT devices with a minimum total
transmit power, a novel framework is proposed for jointly optimizing the
three-dimensional (3D) placement and mobility of the UAVs, device-UAV
association, and uplink power control. First, given the locations of active IoT
devices at each time instant, the optimal UAVs' locations and associations are
determined. Next, to dynamically serve the IoT devices in a time-varying
network, the optimal mobility patterns of the UAVs are analyzed. To this end,
based on the activation process of the IoT devices, the time instances at which
the UAVs must update their locations are derived. Moreover, the optimal 3D
trajectory of each UAV is obtained in a way that the total energy used for the
mobility of the UAVs is minimized while serving the IoT devices. Simulation
results show that, using the proposed approach, the total transmit power of the
IoT devices is reduced by 45% compared to a case in which stationary aerial
base stations are deployed. In addition, the proposed approach can yield a
maximum of 28% enhanced system reliability compared to the stationary case. The
results also reveal an inherent tradeoff between the number of update times,
the mobility of the UAVs, and the transmit power of the IoT devices. In
essence, a higher number of updates can lead to lower transmit powers for the
IoT devices at the cost of an increased mobility for the UAVs.Comment: Accepted in IEEE Transactions on Wireless Communications, Sept. 201
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