  The open radio access network (O-RAN) architecture offers a cost-effective
and scalable solution for internet service providers to optimize their networks
using machine learning algorithms. The architecture's open interfaces enable
network function virtualization, with the O-RAN serving as the primary
communication device for users. However, the limited frequency resources and
information explosion make it difficult to achieve an optimal network
experience without effective traffic control or resource allocation. To address
this, we consider mobility-aware load balancing to evenly distribute loads
across the network, preventing network congestion and user outages caused by
excessive load concentration on open radio unit (O-RU) governed by a single
open distributed unit (O-DU). We have proposed a multi-agent multi-armed bandit
for load balancing and resource allocation (mmLBRA) scheme, designed to both
achieve load balancing and improve the effective sum-rate performance of the
O-RAN network. We also present the mmLBRA-LB and mmLBRA-RA sub-schemes that can
operate independently in non-realtime RAN intelligent controller (Non-RT RIC)
and near-RT RIC, respectively, providing a solution with moderate loads and
high-rate in O-RUs. Simulation results show that the proposed mmLBRA scheme
significantly increases the effective network sum-rate while achieving better
load balancing across O-RUs compared to rule-based and other existing heuristic
methods in open literature.
