A Regularized Routing Optimization Approach for Enhanced Throughput and Low Latency with Efficient Complexity in Communication Networks

In the fast-evolving world of wireless networks, achieving high throughput with low latency is essential for future communication systems. Although low-complexity OSPF-type solutions are effective in lightly-loaded networks, their performance tends to degrade as congestion increases. Recent methods have proposed using backpressure and deep learning for route optimization, but these approaches face challenges due to their high implementation and computational complexity, which may exceed the capabilities of networks with limited hardware resources. A key challenge is developing algorithms that improve throughput and reduce latency while keeping complexity levels compatible with OSPF. In this paper, 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 tech-nologies, 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.