1,196 research outputs found
Echo State Learning for Wireless Virtual Reality Resource Allocation in UAV-enabled LTE-U Networks
In this paper, the problem of resource management is studied for a network of
wireless virtual reality (VR) users communicating using an unmanned aerial
vehicle (UAV)-enabled LTE-U network. In the studied model, the UAVs act as VR
control centers that collect tracking information from the VR users over the
wireless uplink and, then, send the constructed VR images to the VR users over
an LTE-U downlink. Therefore, resource allocation in such a UAV-enabled LTE-U
network must jointly consider the uplink and downlink links over both licensed
and unlicensed bands. In such a VR setting, the UAVs can dynamically adjust the
image quality and format of each VR image to change the data size of each VR
image, then meet the delay requirement. Therefore, resource allocation must
also take into account the image quality and format. This VR-centric resource
allocation problem is formulated as a noncooperative game that enables a joint
allocation of licensed and unlicensed spectrum bands, as well as a dynamic
adaptation of VR image quality and format. To solve this game, a learning
algorithm based on the machine learning tools of echo state networks (ESNs)
with leaky integrator neurons is proposed. Unlike conventional ESN based
learning algorithms that are suitable for discrete-time systems, the proposed
algorithm can dynamically adjust the update speed of the ESN's state and,
hence, it can enable the UAVs to learn the continuous dynamics of their
associated VR users. Simulation results show that the proposed algorithm
achieves up to 14% and 27.1% gains in terms of total VR QoE for all users
compared to Q-learning using LTE-U and Q-learning using LTE
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