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Title: MIA: A Transport-Layer Plugin for Immersive Applications in Millimeter Wave Access Networks
The highly directional nature of the millimeter wave (mmWave) beams pose several challenges in using that spectrum for meeting the communication needs of immersive applications. In particular, the mmWave beams are susceptible to misalignments and blockages caused by user movements. As a result, mmWave channels are vulnerable to large fluctuations in quality, which in turn, cause disproportionate degradation in end-to-end performance of Transmission Control Protocol (TCP) based applications. In this paper, we propose a reinforcement learning (RL) integrated transport-layer plugin, Millimeter wave based Immersive Agent (MIA), for immersive content delivery over the mmWave link. MIA uses the RL model to predict mmWave link bandwidth based on the real-time measurement. Then, MIA cooperates with TCP’s congestion control scheme to adapt the sending rate in accordance with the predictions of the mmWave bandwidth. To evaluate the effectiveness of the proposed MIA, we conduct experiments using a mmWave augmented immersive testbed and network simulations. The evaluation results show that MIA improves end-to-end immersive performance significantly on both throughput and latency.  more » « less
Award ID(s):
1703389
PAR ID:
10525302
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE
Date Published:
ISBN:
979-8-3503-3414-2
Page Range / eLocation ID:
1 to 10
Subject(s) / Keyword(s):
Adaptation models Fluctuations Protocols Systems architecture Bandwidth Reinforcement learning Predictive models mmWave immersive applications bandwidth prediction reinforcement learning TCP
Format(s):
Medium: X
Location:
New York City, NY, USA
Sponsoring Org:
National Science Foundation
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