Unsupervised Machine Learning-Based User Clustering in Millimeter-Wave-NOMA Systems
Unsupervised Machine Learning-Based User Clustering in Millimeter-Wave-NOMA Systems
复制标题
毫米波 NOMA 系统中基于无监督机器学习的用户聚类
DOI：
10.1109/twc.2018.2867180
复制
发表时间：
2018-11-01
影响因子：
10.4
通讯作者：
Al-Dhahir, Naofal
中科院分区：
文献类型：
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作者：
Cui, Jingjing;Ding, Zhiguo;Al-Dhahir, Naofal
毫米波非正交多址接入（mm-wave-NOMA）系统利用功率域进行多址接入以进一步提高频谱效率。在毫米波系统中，用户分簇和功率分配可以有效地发挥NOMA的潜力。本文研究了在总发射功率和用户预定速率要求约束下，毫米波NOMA系统的和速率最大化问题。公式化的优化问题是一个非线性规划问题，因此，是非凸的和具有挑战性的解决，特别是当用户的数量变得很大。针对毫米波NOMA系统中用户信道的相关性特点，提出了一种基于K均值的机器学习用户聚类算法。此外，对于一个实际的动态场景，新用户不断到达一个连续的方式，我们提出了一个基于K-均值的在线用户聚类算法，以减少计算复杂度。此外，为了进一步提高所提出的毫米波NOMA系统的性能，我们推导出最佳的功率分配策略，在一个封闭的形式，利用连续解码功能。仿真结果表明：1）与传统的用户聚类算法相比，所提出的机器学习框架增强了mm波NOMA系统的性能，并且2）所提出的基于K均值的在线用户聚类算法提供了与传统K均值算法相当的性能，并且在性能和计算复杂度之间取得了良好的平衡。
Millimeter-wave non-orthogonal multiple access (mm-wave-NOMA) systems exploit the power domain for multiple accesses to further enhance the spectral efficiency. User clustering and power allocation can effectively exploit the potential of NOMA in mm-wave systems. This paper investigates the sum rate maximization problem of mm-wave-NOMA systems under the constraints of the total transmission power and users' predefined rate requirements. The formulated optimization problem is a non-linear programming problem and, thus, is non-convex and challenging to solve, especially when the number of users becomes large. Sparked by the correlation features of the users' channels in mm-wave-NOMA systems, we develop a K-means-based machine learning algorithm for user clustering. Moreover, for a practical dynamic scenario where the new users keep arriving in a continuous fashion, we propose a K-means-based online user clustering algorithm to reduce the computational complexity. Furthermore, to further enhance the performance of the proposed mm-wave-NOMA system, we derive the optimal power allocation policy in a closed form by exploiting the successive decoding feature. Simulation results reveal that: 1) the proposed machine learning framework enhances the performance of mm-wave-NOMA systems compared to the conventional user clustering algorithms and 2) the proposed K-means-based online user clustering algorithm provides a comparable performance to the conventional K-means algorithm and strikes a good balance between performance and computational complexity.