2,106 research outputs found
Channel Assignment in Uplink Wireless Communication using Machine Learning Approach
This letter investigates a channel assignment problem in uplink wireless
communication systems. Our goal is to maximize the sum rate of all users
subject to integer channel assignment constraints. A convex optimization based
algorithm is provided to obtain the optimal channel assignment, where the
closed-form solution is obtained in each step. Due to high computational
complexity in the convex optimization based algorithm, machine learning
approaches are employed to obtain computational efficient solutions. More
specifically, the data are generated by using convex optimization based
algorithm and the original problem is converted to a regression problem which
is addressed by the integration of convolutional neural networks (CNNs),
feed-forward neural networks (FNNs), random forest and gated recurrent unit
networks (GRUs). The results demonstrate that the machine learning method
largely reduces the computation time with slightly compromising of prediction
accuracy
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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