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.

翻译：在无线网络快速发展的背景下，实现高吞吐量与低延迟的数据传输对未来通信系统至关重要。虽然低复杂度的OSPF类解决方案在轻负载网络中表现出色，但在面对日益增长的拥塞时往往表现不佳。近期研究提出了利用背压和深度学习技术进行路由优化的方法。然而，这些方法因其实施和计算复杂度较高而面临挑战，超出了硬件设备受限网络的处理能力。关键挑战在于开发既能提升吞吐量、降低延迟，又能保持与OSPF相当复杂度水平的算法。在本项本-古里安大学与Ceragon Networks Ltd.的合作研究中，我们通过提出一种名为正则化路由优化（RRO）的新方法应对这一挑战。RRO算法提供分布式与集中式两种低复杂度实现方案，适用于5G及后续技术的集成，且无需对现有协议进行重大改动。该算法通过正则化优化在确保延迟足够低的同时提升吞吐量。我们分析了RRO的计算复杂度，并证明其收敛复杂度与OSPF相当。在不同网络拓扑结构下的广泛仿真结果表明，RRO显著优于现有方法。