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

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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.

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Notes

  1. We argue that the way we modeled our problem and configured our Q-Learning algorithm does not require a complex reinforcement learning solution. This is two fold. First, the Wi-Fi AP operation mode has a passive influence that is accounted for the MDP just as part of the environment. In other words, it is not a multi-agent competition scenario. In fact, the LTE-U DM-CSAT is the only one to perform decisions. Second, we applied a restricted quantity of states and actions, such that updating the Q-Table does not become ineffective.

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Authors and Affiliations

  1. Samsung R&D Institute (SIDIA), Manaus, AM, Brazil

    Pedro M. de Santana & Fuad M. Abinader Jr.

  2. Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil

    Vicente A. de Sousa Jr. & José M. de C. Neto

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  1. Pedro M. de Santana
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  2. Vicente A. de Sousa Jr.
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  3. Fuad M. Abinader Jr.
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  4. José M. de C. Neto
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Correspondence to Pedro M. de Santana.

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The proof of concept simulations provided by this paper was supported by High Performance Computing Center at UFRN (NPAD/UFRN). Fuad M. Abinader Jr. is currently with Nokia Bell Labs, Paris, France. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.

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de Santana, P.M., de Sousa, V.A., Abinader, F.M. et al. DM-CSAT: a LTE-U/Wi-Fi coexistence solution based on reinforcement learning. Telecommun Syst 71, 615–626 (2019). https://doi.org/10.1007/s11235-018-00535-7

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