  In the rapidly evolving landscape of wireless networks, achieving enhanced
throughput with low latency for data transmission is crucial for future
communication systems. While low complexity OSPF-type solutions have shown
effectiveness in lightly-loaded networks, they often falter in the face of
increasing congestion. Recent approaches have suggested utilizing backpressure
and deep learning techniques for route optimization. However, these approaches
face challenges due to their high implementation and computational complexity,
surpassing the capabilities of networks with limited hardware devices.
  A key challenge is developing algorithms that improve throughput and reduce
latency while keeping complexity levels compatible with OSPF. In this
collaborative research between Ben-Gurion University and Ceragon Networks Ltd.,
we address this challenge by developing a novel approach, dubbed Regularized
Routing Optimization (RRO). The RRO algorithm offers both distributed and
centralized implementations with low complexity, making it suitable for
integration into 5G and beyond technologies, where no significant changes to
the existing protocols are needed. It increases throughput while ensuring
latency remains sufficiently low through regularized optimization. We analyze
the computational complexity of RRO and prove that it converges with a level of
complexity comparable to OSPF. Extensive simulation results across diverse
network topologies demonstrate that RRO significantly outperforms existing
methods.
