Diógenes do Rego Iago, de Castro Neto José M, Neto Sildolfo F G, de Santana Pedro M, de Sousa Vicente A, Vieira Dario, Venâncio Neto Augusto
PPgEEC, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil.
Efrei Research Lab, EFREI Paris, 94800 Villejuif, France.
Sensors (Basel). 2023 Jul 27;23(15):6718. doi: 10.3390/s23156718.
Recent studies and literature reviews have shown promising results for 3GPP system solutions in unlicensed bands when coexisting with Wi-Fi, either by using the duty cycle (DC) approach or licensed-assisted access (LAA). However, it is widely known that general performance in these coexistence scenarios is dependent on traffic and how the duty cycle is adjusted. Most DC solutions configure their parameters statically, which can result in performance losses when the scenario experiences changes on the offered data. In our previous works, we demonstrated that reinforcement learning (RL) techniques can be used to adjust DC parameters. We showed that a Q-learning (QL) solution that adapts the LTE DC ratio to the transmitted data rate can maximize the Wi-Fi/LTE-Unlicensed (LTE-U) aggregated throughput. In this paper, we extend our previous solution by implementing a simpler and more efficient algorithm based on multiarmed bandit (MAB) theory. We evaluate its performance and compare it with the previous one in different traffic scenarios. The results demonstrate that our new solution offers improved balance in throughput, providing similar results for LTE and Wi-Fi, while still showing a substantial system gain. Moreover, in one of the scenarios, our solution outperforms the previous approach by 6% in system throughput. In terms of user throughput, it achieves more than 100% gain for the users at the 10th percentile of performance, while the old solution only achieves a 10% gain.
最近的研究和文献综述表明，当3GPP系统解决方案在非授权频段与Wi-Fi共存时，无论是采用占空比（DC）方法还是许可辅助接入（LAA），都能取得令人满意的结果。然而，众所周知，这些共存场景中的总体性能取决于流量以及占空比的调整方式。大多数DC解决方案静态配置其参数，当场景中提供的数据发生变化时，这可能会导致性能损失。在我们之前的工作中，我们证明了强化学习（RL）技术可用于调整DC参数。我们表明，一种将LTE DC比率与传输数据速率相适配的Q学习（QL）解决方案可以使Wi-Fi/LTE非授权（LTE-U）聚合吞吐量最大化。在本文中，我们通过基于多臂赌博机（MAB）理论实现一种更简单、更高效的算法来扩展我们之前的解决方案。我们评估其性能，并在不同流量场景中将其与之前的解决方案进行比较。结果表明，我们的新解决方案在吞吐量方面实现了更好的平衡，为LTE和Wi-Fi提供了相似的结果，同时仍显示出显著的系统增益。此外，在其中一个场景中，我们的解决方案在系统吞吐量方面比之前的方法高出6%。在用户吞吐量方面，对于性能处于第10百分位的用户，它实现了超过100%的增益，而旧解决方案仅实现了10%的增益。
