论文标题
基于聚类的关节通道估计和无授予Noma的信号检测
Clustering-based Joint Channel Estimation and Signal Detection for Grant-free NOMA
论文作者
论文摘要
我们建议使用无监督的聚类方法为上行链路非正交多访问的联合通道估计和信号检测技术。我们将高斯混合模型应用于聚类的信号，因此优化了决策区域以提高符号错误率（SER）。我们表明，当接收到的用户的功率足够不同时，在接收器处没有渠道状态信息（CSI）的提议的基于聚类的方法可以实现类似于带有完整CSI的常规最大似然检测器的SER性能。由于使用的聚类算法的准确性取决于接收器中可用的数据点的数量，因此所提出的技术可以在精度和块长度之间进行权衡。
We propose a joint channel estimation and signal detection technique for the uplink non-orthogonal multiple access using an unsupervised clustering approach. We apply the Gaussian mixture model to cluster 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 with no channel state information (CSI) at the receiver achieves an SER performance similar to that of the conventional maximum likelihood detector with full CSI. Since the accuracy of the utilized clustering algorithm depends on the number of the data points available at the receiver, the proposed technique delivers a tradeoff between the accuracy and block length.
