On the Enabling of Efficient Coexistence of LTE With WiFi: A Machine Learning-Based Approach

Mohamed S. Hassan, M. H. Ismail, M. El-Tarhuni, Fatema Aseeri
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Abstract

The recently proposed extension of the LTE operation to the unlicensed spectrum, known as LTE-Unlicensed (LTE-U), is not only expected to alleviate the congestion in the licensed band but is expected to result in an increase in the network capacity, as well. Unfortunately, such extension is challenged by a coexistence problem with wireless technologies operating in the unlicensed spectrum, especially Wi-Fi. Therefore, this article employs time series forecasting methods to enable efficient LTE coexistence with Wi-Fi. This is done by enabling the LTE-U Home eNodeB (HeNB) to avoid collisions with Wi-Fi by predicting the state of the unlicensed channels prior to using them. Specifically, this research proposes a recurrent neural network-based algorithm that utilizes Long Short Term Memory (LSTM) networks with time series decomposition to predict the state of the channels in the unlicensed spectrum. The authors investigate the performance of the proposed approach using extensive simulations. The results show that the proposed LSTM-based method outperforms the classical Listen Before Talk (LBT) and duty-cycling approaches in terms of improved coexistence of LTE-U with Wi-Fi.
LTE与WiFi高效共存的实现:基于机器学习的方法
最近提出的将LTE业务扩展到未授权频谱,即LTE- unlicensing (LTE- u),不仅有望缓解许可频段的拥塞,而且有望增加网络容量。不幸的是,这种扩展受到在未经许可的频谱中运行的无线技术的共存问题的挑战,特别是Wi-Fi。因此,本文采用时间序列预测方法,实现LTE与Wi-Fi的高效共存。这是通过使LTE-U家庭eNodeB (HeNB)在使用未授权信道之前预测其状态来避免与Wi-Fi发生冲突来实现的。具体而言,本研究提出了一种基于循环神经网络的算法,该算法利用具有时间序列分解的长短期记忆(LSTM)网络来预测无许可频谱中的信道状态。作者通过大量的仿真研究了所提出方法的性能。结果表明,提出的基于lstm的方法在改善LTE-U与Wi-Fi共存方面优于经典的先听后讲(LBT)和占空循环方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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