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We propose a joint channel estimation and signal detection technique for theuplink non-orthogonal multiple access using an unsupervised clusteringapproach. We apply the Gaussian mixture model to cluster received signals andaccordingly optimize the decision regions to enhance the symbol error rate(SER). We show that when the received powers of the users are sufficientlydifferent, the proposed clustering-based approach with no channel stateinformation (CSI) at the receiver achieves an SER performance similar to thatof the conventional maximum likelihood detector with full CSI. Since theaccuracy of the utilized clustering algorithm depends on the number of the datapoints available at the receiver, the proposed technique delivers a tradeoffbetween the accuracy and block length.

Clustering-based Joint Channel Estimation and Signal Detection for Grant-free NOMA