This paper aims to develop resilient transmission mechanisms to suitably distribute traffic across multiple paths in an arbitrary millimeter-wave (mmWave) network. The main contributions include: (a) the development of proactive transmission mechanisms that build resilience against network disruptions in advance, while achieving a high end-to-end packet rate; (b) the design of a heuristic path selection algorithm that efficiently selects (in polynomial time in the network size) multiple proactively resilient paths with high packet rates; and (c) the development of a hybrid scheduling algorithm that combines the proposed path selection algorithm with a deep reinforcement learning (DRL) based online approach for decentralized adaptation to blocked links and failed paths. To achieve resilience to link failures, a state-of-the-art Soft Actor-Critic DRL algorithm, which adapts the information flow through the network, is investigated. The proposed scheduling algorithm robustly adapts to link failures over different topologies, channel and blockage realizations while offering a superior performance to alternative algorithms.
翻译:本文旨在开发有弹性的传输机制,以便在任意毫米波(mmWave)网络中适当分布多条路径的交通流量,主要贡献包括:(a) 开发积极主动的传输机制,建立应对网络中断的抗御能力,同时实现高端到终端的包速率;(b) 设计一种湿道选择算法,以高效选择(在网络规模的多元时间里)多条具有弹性的多条路径和高封包率;(c) 开发一种混合排期算法,将拟议的路径选择算法与基于深度强化学习(DRL)的在线学习法相结合,以分散适应被阻断的链接和失败的路径。为了实现连接故障的复原力,正在调查调整网络信息流动的先进软动作-Critric DRL算法。拟议的排期算法将不同地形、频道和障碍实现的失败和失败联系起来,同时提供优异功能以替代算法。