Liquid State Machine Learning for Resource and Cache Management in LTE-U Unmanned Aerial Vehicle (UAV) Networks
Liquid State Machine Learning for Resource and Cache Management in LTE-U Unmanned Aerial Vehicle (UAV) Networks
复制标题
用于 LTE-U 无人机 (UAV) 网络中资源和缓存管理的液态状态机器学习
DOI：
10.1109/twc.2019.2891629
复制
发表时间：
2019-03-01
影响因子：
10.4
通讯作者：
Yin, Changchuan
中科院分区：
文献类型：
--
作者：
Chen, Mingzhe;Saad, Walid;Yin, Changchuan
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，该网络通过LTE许可和非许可频段为无线地面用户提供服务。所考虑的模型侧重于可以访问许可和非许可频段的用户，同时直接或通过内容服务器-无人机-用户链路从无人机处的高速缓存单元接收内容。这个问题被描述为一个优化问题，它联合地结合了用户关联、频谱分配和内容缓存。针对这一问题，提出了一种基于液体状态机(LSM)机器学习框架的分布式算法。使用提出的LSM算法，云可以预测用户的内容请求分布，而只有关于网络和用户状态的有限信息。该算法还使得无人机能够根据网络状态自主地选择最优的资源分配策略，从而最大化具有稳定队列的用户数量。基于用户关联和内容请求分布，推导出无人机需要缓存的最优内容和最优资源分配。在真实数据集上的仿真结果表明，与有缓存的Q学习算法和无缓存的Q学习算法相比，该方法在拥有稳定队列的用户数方面分别获得了17.8%和57.1%的收益。实验结果还表明，与Q学习等传统学习算法相比，最小二乘学习算法的收敛速度提高了20%。
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 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 and the optimal resource allocation are derived. Simulation results using real datasets show that the proposed approach yields up to 17.8% and 57.1% gains, respectively, in terms of the number of users that have stable queues compared with two baseline algorithms: Q-learning with cache and Q-learning without cache. The results also show that the LSM significantly improves the convergence time of up to 20% compared with conventional learning algorithms such as Q-learning.