  Explosive growth in the use of smart wireless devices has necessitated the
provision of higher data rates and always-on connectivity, which are the main
motivators for designing the fifth generation (5G) systems. To achieve higher
system efficiency, massive antenna deployment with tight coordination is one
potential strategy for designing 5G systems, but has two types of associated
system overhead. First is the synchronization overhead, which can be reduced by
implementing a cloud radio access network (CRAN)-based architecture design,
that separates the baseband processing and radio access functionality to
achieve better system synchronization. Second is the overhead for acquiring
channel state information (CSI) of the users present in the system, which,
however, increases tremendously when instantaneous CSI is used to serve
high-mobility users. To serve a large number of users, a CRAN system with a
dense deployment of remote radio heads (RRHs) is considered, such that each
user has a line-of-sight (LOS) link with the corresponding RRH. Since, the
trajectory of movement for high-mobility users is predictable; therefore,
fairly accurate position estimates for those users can be obtained, and can be
used for resource allocation to serve the considered users. The resource
allocation is dependent upon various correlated system parameters, and these
correlations can be learned using well-known \emph{machine learning}
algorithms. This paper proposes a novel \emph{learning-based resource
allocation scheme} for time division duplex (TDD) based 5G CRAN systems with
dense RRH deployment, by using only the users' position estimates for resource
allocation, thus avoiding the need for CSI acquisition. This reduces the
overall system overhead significantly, while still achieving near-optimal
system performance; thus, better (effective) system efficiency is achieved.
(See the paper for full abstract)
