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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.
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Measuring Millimeter Wave based Link Bandwidth Fluctuations during Indoor Immersive Experience
This paper presents the results of motion-tracking synchronized millimeter wave (mmWave) link bandwidth fluctuations while a user is engaged in immersive augmented/virtual reality applications. Our system, called MITRAS, supports ex- tensive exploration of human-induced impacts on mmWave link bandwidth during immersive experience. MITRAS adopts the packet train measurement application to track link bandwidth fluctuations. Meanwhile, the user movements are tracked using an Oculus Quest 2 headset. Through investigating the impacts of human movements on link bandwidth fluctuations, we further propose a link state prediction model to shed light on higher layer protocol design for immersive applications over mmWave links.
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- Award ID(s):
- 1703389
- PAR ID:
- 10341731
- Date Published:
- Journal Name:
- IEEE Networking Letters
- ISSN:
- 2576-3156
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/LNET.2022.3157324
