DM-CSAT: a LTE-U/Wi-Fi coexistence solution based on reinforcement learning

Pedro M. Santana (), Vicente A. Sousa (), Fuad M. Abinader () and José M. C. Neto ()
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Pedro M. Santana: Samsung R&D Institute (SIDIA)
Vicente A. Sousa: Federal University of Rio Grande do Norte (UFRN)
Fuad M. Abinader: Samsung R&D Institute (SIDIA)
José M. C. Neto: Federal University of Rio Grande do Norte (UFRN)

Telecommunication Systems: Modelling, Analysis, Design and Management, 2019, vol. 71, issue 4, No 7, 615-626

Abstract: Abstract Recent literature demonstrated promising results of Long-Term Evolution (LTE) deployments over unlicensed bands when coexisting with Wi-Fi networks via the Duty-Cycle (DC) approach. However, it is known that performance in coexistence is strongly dependent on traffic patterns and on the duty-cycle ON–OFF rate of LTE. Most DC solutions rely on static coexistence parameters configuration, hence real-life performance in dynamically varying scenarios might be affected. Advanced reinforcement learning techniques may be used to adjust DC parameters towards efficient coexistence, and we propose a Q-learning Carrier-Sensing Adaptive Transmission mechanism which adapts LTE duty-cycle ON–OFF time ratio to the transmitted data rate, aiming at maximizing the Wi-Fi and LTE-Unlicensed (LTE-U) aggregated throughput. The problem is formulated as a Markov decision process, and the Q-learning solution for finding the best LTE-U ON–OFF time ratio is based on the Bellman’s equation. We evaluate the performance of the proposed solution for different traffic load scenarios using the ns-3 simulator. Results demonstrate the benefits from the adaptability to changing circumstances of the proposed method in terms of Wi-Fi/LTE aggregated throughput, as well as achieving a fair coexistence.

Keywords: LTE; LTE-LAA; LTE-U; Wi-Fi; Q-Learning (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11235-018-00535-7

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