text: abstract—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.