  Big data analytics is one of the state-of-the-art tools to optimize networks
and transform them from merely being a blind tube that conveys data, into a
cognitive, conscious, and self-optimizing entity that can intelligently adapt
according to the needs of its users. This, in fact, can be regarded as one of
the highest forthcoming priorities of future networks. In this paper, we
propose a system for Out-Patient (OP) centric Long Term Evolution-Advanced
(LTE-A) network optimization. Big data harvested from the OPs' medical records,
along with current readings from their body sensors are processed and analyzed
to predict the likelihood of a life-threatening medical condition, for
instance, an imminent stroke. This prediction is used to ensure that the OP is
assigned an optimal LTE-A Physical Resource Blocks (PRBs) to transmit their
critical data to their healthcare provider with minimal delay. To the best of
our knowledge, this is the first time big data analytics are utilized to
optimize a cellular network in an OP-conscious manner. The PRBs assignment is
optimized using Mixed Integer Linear Programming (MILP) and a real-time
heuristic. Two approaches are proposed, the Weighted Sum Rate Maximization
(WSRMax) approach and the Proportional Fairness (PF) approach. The approaches
increased the OPs' average SINR by 26.6% and 40.5%, respectively. The WSRMax
approach increased the system's total SINR to a level higher than that of the
PF approach, however, the PF approach reported higher SINRs for the OPs, better
fairness and a lower margin of error.
