  Appropriate allocation of system resources is essential for meeting the
increased user-traffic demands in the next generation wireless technologies.
Traditionally, the system relies on channel state information (CSI) of the
users for optimizing the resource allocation, which becomes costly for
fast-varying channel conditions. Considering that future wireless technologies
will be based on dense network deployment, where the mobile terminals are in
line-of-sight of the transmitters, the position information of terminals
provides an alternative to estimate the channel condition. In this work, we
propose a coordinates-based resource allocation scheme using supervised machine
learning techniques, and investigate how efficiently this scheme performs in
comparison to the traditional approach under various propagation conditions. We
consider a simplistic system set up as a first step, where a single transmitter
serves a single mobile user. The performance results show that the
coordinates-based resource allocation scheme achieves a performance very close
to the CSI-based scheme, even when the available coordinates of terminals are
erroneous. The proposed scheme performs consistently well with realistic-system
simulation, requiring only 4 s of training time, and the appropriate resource
allocation is predicted in less than 90 microseconds with a learnt model of
size less than 1 kB.
