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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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Proactive Resilience in 1-2-1 Networks
Millimeter Wave (mmWave) (and beyond) is expected to play an increasingly important role in our wireless infrastructure by expanding the available spectrum and enabling multi-gigabit services. Despite the promising aspects of mmWave communication, mmWave links are highly sensitive to blockage. In this paper, we develop proactive transmission mechanisms that suitably distribute the traffic across multiple paths in the mmWave network, with the two-fold objective of ensuring resilience against link blockages and achieve high end-to-end packet delivery rate. We present examples of resilience-capacity trade-off curves and show that there exist network topologies for which the worst-case and average approximate capacities are achieved by activating overlapping paths. We also show that this can provide additional benefits, such as decreasing the variance of the achieved rate.
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- PAR ID:
- 10408552
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE International Symposium on Information Theory (ISIT)
- Volume:
- 1
- Issue:
- 1
- Page Range / eLocation ID:
- 3220 to 3225
- 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
- Conference Paper:
- https://doi.org/10.1109/ISIT50566.2022.9834416
