User Activity And Data Detection For MIMO Uplink C-RAN Using Bayesian Learning

We investigate user activity and data detection problem in a multiple-input multiple-output uplink cloud-radio access network, where the data matrix over a time-frame has overlapped burst sparsity due to sporadic user activity. We exploit this sparsity to recover data by proposing a weighted prior-sparse Bayesian learning algorithm. The proposed algorithm, due to carefully selected prior, captures not only the overlapped burst sparsity across time but also the block sparsity due to multi-user antennas. We also derive hyperparameter updates, and estimate the weight parameters using the support estimated via index-wise log-likelihood ratio test. We numerically demonstrate that the proposed algorithm has much lower bit error rate than the state-of-the-art competing algorithms.