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The dramatic growth in demand for mobile data service has prompted mobile network operators (MNOs) to explore new spectrum resources in unlicensed bands. MNOs have been recently allowed to extend LTE-based service called LTE-LAA over 5 GHz U-NII bands, currently occupied by Wi-Fi. To support applications with diverse QoS requirements, both LTE and Wi-Fi technologies introduce multiple priority classes with different channel contention parameters for accessing unlicensed bands. How these different priority classes affect the interplay between coexisting LTE and Wi-Fi technologies is still relatively under-explored. In this paper, we develop a simple and efficient framework that helps MNOs assess the fair coexistence between MNOs and Wi-Fi operators with prioritized channel access under the multi-channel setting. We derive an approximated closed-form solution for each MNO to pre-evaluate the probability of successful transmission (PST), average contention delay, and average throughput when adopting different priority classes to serve different traffics. MNOs and Wi-Fi operators can fit our model using measurements collected offline and/or online, and use it to further optimize their systems’ throughput and latency. Our results reveal that PSTs computed with our approximated closed-form model approach those collected from system-level simulations with around 95% accuracy under scenarios of dense network deployment density and high traffic intensity.
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Machine Learning Enhanced Channel Selection for Unlicensed LTE
We propose a mechanism for unlicensed LTE channel selection that not only takes into account interference to and from Wi-Fi access points but also considers other LTE operators in the unlicensed band. By collecting channel utilization statistics and sharing this information periodically with other unlicensed LTE eNBs, each eNB can improve their channel selection given their limited knowledge of the full topology. While comparing our algorithm to existing solutions, we find that the similarity between sensed Wi-Fi occupation at neighboring eNBs greatly impacts the performance of channel selection algorithms. To achieve better performance across diverse scenarios, we expand on our statistical channel selection formulation to include reinforcement learning, thereby balancing the shared contextual information with historical performance. We simulate operation in the unlicensed band using our channel selection algorithm and show how Wi-Fi load and inter-cell interference estimation can jointly be used to select transmission channels for all small cells in the network. Our approaches lead to an increase in user-perceived throughput and spectral efficiency across the entire band when compared to the greedy channel selection.
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- Award ID(s):
- 1823304
- PAR ID:
- 10196183
- Date Published:
- Journal Name:
- 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
- Page Range / eLocation ID:
- 1 to 10
- 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/DySPAN.2019.8935859
