LTE-unlicensed (LTE-U) technology is a promising innovation to extend the capacity of cellular networks. The primary challenge for LTE-U is the fair coexistence between LTE systems and the incumbent WiFi systems. In this paper, we aim to maximize the long-term average per-user LTE throughput with long-term fairness guarantee by jointly considering resource allocation and user association on the unlicensed spectrum within a prediction window. We first formulate the problem as an NP-hard combinatorial optimization problem, then reformulate it as a non-cooperative game by applying the penalty function method. To solve the game, a novel reinforcement learning approach based on Bi-directional LSTM neural network is proposed, which enables small base stations (SBSs) to predict a sequence of future actions over the next prediction window based on the historical network information. It is shown that the proposed approach can converge to a mixed-strategy Nash equilibrium of the studied game and ensure the long-term fair coexistence between different access technologies. Finally, the effectiveness of the proposed algorithm is demonstrated by numerical simulation.

LTE非授权（LTE-U）技术是扩展蜂窝网络容量的有前途的创新。LTE-U的主要挑战是LTE系统与现有WiFi系统之间的公平共存。在本文中，我们旨在通过在预测窗口内共同考虑非许可频谱上的资源分配和用户关联，在长期公平性保证的基础上最大化长期平均每用户LTE吞吐量。我们首先将问题表述为NP-hard组合优化问题，然后通过应用罚函数法将其重新表述为非合作博弈。为了解决游戏问题，提出了一种基于双向LSTM神经网络的强化学习新方法，这样，小型基站（SBS）便可以根据历史网络信息在下一个预测窗口中预测一系列未来动作。结果表明，所提出的方法可以收敛到研究博弈的混合策略纳什均衡，并确保不同接入技术之间的长期公平共存。最后，通过数值仿真证明了该算法的有效性。