Deep MIMO Detection Based on Belief Propagation

The belief propagation (BP) algorithm exhibits outstanding detection performance for the multiple-input multiple-output (MIMO) transmission. However, this algorithm may fail to converge because of the fully connected factor graph under the MIMO settings. To address this issue, a novel deep learning detector based on the BP algorithm (DLBP detector) is proposed, which combines the BP algorithm with the deep learning methods. The log likelihood ratios (LLR) messages are passed on the MIMO factor graph to detect the signals from the transmitters. In addition, the weights are trained via the deep learning methods and further assigned to the messages updated in the DLBP detector. Finally, simulations show that, compared with the BP detector and the damped BP detector, the DLBP detector has lower complexity and better bit error rate (BER) performance.