LAUSR

This letter presents a data detection method for multiple-input multiple-output systems with one-bit analog-to-digital converters. The basic idea is to learn the likelihood function of the system from training samples. To this end, a training data generation strategy is first proposed, which labels a one-bit received signal with a symbol index determined by channel-based data detection. This strategy requires no extra training overhead beyond pilot symbols for channel estimation, but leads to noisy labels due to data detection errors. For accurate learning from the noisy labels, an expectation-maximization algorithm is also developed. This algorithm learns both the likelihood function and the transition probability from each noisy label to a true label. Numerical results demonstrate that the presented method performs similar to the optimal maximum likelihood detection.