We propose a joint channel estimation and signal detection approach for the uplink non-orthogonal multiple access using unsupervised machine learning. We apply the Gaussian mixture model to cluster the received signals, and accordingly optimize the decision regions to enhance the symbol error rate (SER). We show that, when the received powers of the users are sufficiently different, the proposed clustering-based approach achieves an SER performance on a par with that of the conventional maximum-likelihood detector with full channel state information. However, unlike the proposed approach, the maximum-likelihood detector requires the transmission of a large number of pilot symbols to accurately estimate the channel. The accuracy of the utilized clustering algorithm depends on the number of the data points available at the receiver. Therefore, there exists a tradeoff between accuracy and block length. We provide a comprehensive performance analysis of the proposed approach as well as deriving a theoretical bound on its SER performance as a function of the block length. Our simulation results corroborate the effectiveness of the proposed approach and verify that the calculated theoretical bound can predict the SER performance of the proposed approach well.
翻译:我们建议采用无人监督的机器学习方法,对非横向上链接多重访问采用联合频道估计和信号探测方法;我们采用高斯混合模型对接收信号进行分组,从而优化决策区域,以提高符号误差率(SER);我们表明,当用户收到的权力差异很大时,拟议的集群方法在与常规最大类似性探测仪的同等水平上实现了SER性能,并提供了完整的频道状态信息;然而,与拟议方法不同,最大类似性能探测器需要传输大量试点符号,以准确估计频道。 使用的集群算法的准确性取决于接收器现有数据点的数目。因此,在准确性和区段长度之间存在着一种权衡。我们提供了对拟议方法的全面性能分析,并从理论上将SER性能作为区长的函数加以约束。我们的模拟结果证实了拟议方法的有效性,并核实计算出的理论约束能够预测拟议方法的SER性能。