论文标题
使用机器学习的用户聚类用于汇率分配
User Clustering for Rate Splitting using Machine Learning
论文作者
论文摘要
Hierarchical Rate Splitting (HRS) schemes proposed in recent years have shown to provide significant improvements in exploiting spatial diversity in wireless networks and provide high throughput for all users while minimising interference among them.因此，此类HRS方案的主要挑战之一是仅根据其渠道状态信息（CSI）了解这些用户的最佳聚类。已知这种聚类问题很难NP，并且要处理找到最佳解决方案的难以管理的复杂性，在这项工作中，提出了基于神经网络（NN）的可扩展且更轻的聚类机制。准确性和性能指标表明，NN能够根据嘈杂的频道响应来学习和聚集用户，并能够达到与文献中其他更复杂的聚类方案相当的速率。
Hierarchical Rate Splitting (HRS) schemes proposed in recent years have shown to provide significant improvements in exploiting spatial diversity in wireless networks and provide high throughput for all users while minimising interference among them. Hence, one of the major challenges for such HRS schemes is the necessity to know the optimal clustering of these users based only on their Channel State Information (CSI). This clustering problem is known to be NP hard and, to deal with the unmanageable complexity of finding an optimal solution, in this work a scalable and much lighter clustering mechanism based on Neural Network (NN) is proposed. The accuracy and performance metrics show that the NN is able to learn and cluster the users based on the noisy channel response and is able to achieve a rate comparable to other more complex clustering schemes from the literature.
