5 research outputs found
Echo State Transfer Learning for Data Correlation Aware Resource Allocation in Wireless Virtual Reality
In this paper, the problem of data correlation-aware resource management is
studied for a network of wireless virtual reality (VR) users communicating over
cloud-based small cell networks (SCNs). In the studied model, small base
stations (SBSs) with limited computational resources act as VR control centers
that collect the tracking information from VR users over the cellular uplink
and send them to the VR users over the downlink. In such a setting, VR users
may send or request correlated or similar data (panoramic images and tracking
data). This potential spatial data correlation can be factored into the
resource allocation problem to reduce the traffic load in both uplink and
downlink. This VR resource allocation problem is formulated as a noncooperative
game that allows jointly optimizing the computational and spectrum resources,
while being cognizant of the data correlation. To solve this game, a transfer
learning algorithm based on the machine learning framework of echo state
networks (ESNs) is proposed. Unlike conventional reinforcement learning
algorithms that must be executed each time the environment changes, the
proposed algorithm can intelligently transfer information on the learned
utility, across time, to rapidly adapt to environmental dynamics due to factors
such as changes in the users' content or data correlation. Simulation results
show that the proposed algorithm achieves up to 16.7% and 18.2% gains in terms
of delay compared to the Q-learning with data correlation and Q-learning
without data correlation. The results also show that the proposed algorithm has
a faster convergence time than Q-learning and can guarantee low delays.Comment: This paper has been accepted by Asiloma