  Open radio access network (ORAN) Alliance offers a disaggregated RAN
functionality built using open interface specifications between blocks. To
efficiently support various competing services, \textit{namely} enhanced mobile
broadband (eMBB) and ultra-reliable and low-latency (uRLLC), the ORAN Alliance
has introduced a standard approach toward more virtualized, open and
intelligent networks. To realize benefits of ORAN in optimizing resource
utilization, this paper studies an intelligent traffic steering (TS) scheme
within the proposed disaggregated ORAN architecture. For this purpose, we
propose a joint intelligent traffic prediction, flow-split distribution,
dynamic user association and radio resource management (JIFDR) framework in the
presence of unknown dynamic traffic demands. To adapt to dynamic environments
on different time scales, we decompose the formulated optimization problem into
two long-term and short-term subproblems, where the optimality of the later is
strongly dependent on the optimal dynamic traffic demand. We then apply a
long-short-term memory (LSTM) model to effectively solve the long-term
subproblem, aiming to predict dynamic traffic demands, RAN slicing, and
flow-split decisions. The resulting non-convex short-term subproblem is
converted to a more computationally tractable form by exploiting successive
convex approximations. Finally, simulation results are provided to demonstrate
the effectiveness of the proposed algorithms compared to several well-known
benchmark schemes.
