Proactive Resilient Transmission and Scheduling Mechanisms for mmWave Networks

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 stateof-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. M. G. Dogan and C. Fragouli are with the Electrical and Computer Engineering Department at the University of California, Los Angeles, CA 90095 USA (e-mail: {minedogan96, christina.fragouli}@ucla.edu). The research carried out at UCLA was supported in part by the Army Research Laboratory under Co-Operative Agreement W911NF-17-2-0196 and by the U.S. National Science Foundation (NSF) awards 442521-FC-22071 and 442521-FC-21454. M. Cardone is with the Electrical and Computer Engineering Department of the University of Minnesota, MN 55404 USA (e-mail: cardo089@umn.edu). The work of M. Cardone was supported in part by the NSF under Grants CCF-2045237 and CNS-2146838. Part of this work was presented at the 2021 IEEE Military Communications Conference [1] and at the 2022 IEEE International Symposium on Information Theory [2]. Millimeter Wave (mmWave) (and beyond) is an enabling technology that is playing an increasingly important role in our wireless infrastructure by expanding the available spectrum and enabling multi-gigabit services [3]-[5]. A number of use cases are currently built around multihop mmWave networks, such as Facebook's Terragraph network [6] that uses flexible mmWave backbones to connect clusters of base stations. Other example scenarios include private networks, such as in shopping centers, airports and enterprises; mmWave mesh networks that use mmWave links as backhaul in dense urban scenarios; military applications employing mobile hot spots; and mmWave based vehicle-to-everything (V2X) services, such as cooperative perception [7]-[9].