Multipath Transmission Scheduling in Millimeter Wave Cloud Radio Access Networks

TL;DR

This paper introduces a multipath transmission scheduling framework for all-mmWave cloud-RANs, improving delay and packet drop rates by leveraging online learning for dynamic RRH association and scheduling.

Contribution

It proposes a novel multipath transmission framework with an online learning scheme for joint RRH association and scheduling in all-mmWave cloud-RANs, addressing intermittent connectivity issues.

Findings

Outperforms baseline schemes in reducing queue length.

Achieves lower packet dropping rates.

Effective in dynamic network conditions.

Abstract

Millimeter wave (mmWave) communications provide great potential for next-generation cellular networks to meet the demands of fast-growing mobile data traffic with plentiful spectrum available. However, in a mmWave cellular system, the shadowing and blockage effects lead to the intermittent connectivity, and the handovers are more frequent. This paper investigates an ``all-mmWave'' cloud radio access network (cloud-RAN), in which both the fronthaul and the radio access links operate at mmWave. To address the intermittent transmissions, we allow the mobile users (MUs) to establish multiple connections to the central unit over the remote radio heads (RRHs). Specifically, we propose a multipath transmission framework by leveraging the ``all-mmWave'' cloud-RAN architecture, which makes decisions of the RRH association and the packet transmission scheduling according to the time-varying network statistics, such that a MU experiences the minimum queueing delay and packet drops. The joint RRH association and transmission scheduling problem is formulated as a Markov decision process (MDP). Due to the problem size, a low-complexity online learning scheme is put forward, which requires no a priori statistic information of network dynamics. Simulations show that our proposed scheme outperforms the state-of-art baselines, in terms of average queue length and average packet dropping rate.