1,352 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
A Multi-Game Framework for Harmonized LTE-U and WiFi Coexistence over Unlicensed Bands
The introduction of LTE over unlicensed bands (LTE-U) will enable LTE base
stations (BSs) to boost their capacity and offload their traffic by exploiting
the underused unlicensed bands. However, to reap the benefits of LTE-U, it is
necessary to address various new challenges associated with LTE-U and WiFi
coexistence. In particular, new resource management techniques must be
developed to optimize the usage of the network resources while handling the
interdependence between WiFi and LTE users and ensuring that WiFi users are not
jeopardized. To this end, in this paper, a new game theoretic tool, dubbed as
\emph{multi-game} framework is proposed as a promising approach for modeling
resource allocation problems in LTE-U. In such a framework, multiple,
co-existing and coupled games across heterogeneous channels can be formulated
to capture the specific characteristics of LTE-U. Such games can be of
different properties and types but their outcomes are largely interdependent.
After introducing the basics of the multi-game framework, two classes of
algorithms are outlined to achieve the new solution concepts of multi-games.
Simulation results are then conducted to show how such a multi-game can
effectively capture the specific properties of LTE-U and make of it a
"friendly" neighbor to WiFi.Comment: Accepted for publication at IEEE Wireless Communications Magazine,
Special Issue on LTE in Unlicensed Spectru
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