基于液态机器学习的LTE-U无人机（UAV）网络资源和缓存管理
Liquid State Machine Learning for Resource and Cache Management in LTE-U Unmanned Aerial Vehicle (UAV) Networks
摘要：
本文研究了支持缓存的无人机（UAV）网络的联合缓存和资源分配问题，这些无人机通过LTE许可和非许可（LTE-U）频段为无线地面用户提供服务。所考虑的模型侧重于用户，可以访问授权和未授权的频带，同时接收内容从缓存单元在无人机直接或通过内容服务器-无人机-用户链接。这个问题被制定为一个优化问题，它联合采用用户关联，频谱分配，和内容缓存。针对这一问题，提出了一种基于液态机（LSM）机器学习框架的分布式算法。使用所提出的LSM算法，云可以预测用户的内容请求分布，而只有有限的网络和用户的状态信息。所提出的算法还使无人机自主选择最佳的资源分配策略，最大限度地提高稳定队列的用户数量取决于网络状态。基于用户关联和内容请求分布，得到了需要在无人机上缓存的最优内容以及最优资源分配。使用真实数据集的仿真结果表明，所提出的方法分别产生高达33.3%和50.3%的收益，在用户的数量方面，有稳定的队列相比，两个基线算法：Q学习与缓存和Q学习没有缓存。结果还表明，LSM显着提高了收敛时间高达33.3%相比，传统的学习算法，如Q学习。
英文摘要：
In this paper, the problem of joint caching and resource allocation is investigated for a network of cache-enabled unmanned aerial vehicles (UAVs) that service wireless ground users over the LTE licensed and unlicensed (LTE-U) bands. The considered model focuses on users that can access both licensed and unlicensed bands while receiving contents from either the cache units at the UAVs directly or via content server-UAV-user links. This problem is formulated as an optimization problem which jointly incorporates user association, spectrum allocation, and content caching. To solve this problem, a distributed algorithm based on the machine learning framework of liquid state machine (LSM) is proposed. Using the proposed LSM algorithm, the cloud can predict the users' content request distribution while having only limited information on the network's and users' states. The proposed algorithm also enables the UAVs to autonomously choose the optimal resource allocation strategies that maximize the number of users with stable queues depending on the network states. Based on the users' association and content request distributions, the optimal contents that need to be cached at UAVs as well as the optimal resource allocation are derived. Simulation results using real datasets show that the proposed approach yields up to 33.3% and 50.3% gains, respectively, in terms of the number of users that have stable queues compared to two baseline algorithms: Q-learning with cache and Q-learning without cache. The results also show that LSM significantly improves the convergence time of up to 33.3% compared to conventional learning algorithms such as Q-learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
