Space-Partition-Driven Learning Network for Efficient MIMO Detection

With the development of communication systems, the MIMO order of low-cost communication equipments have also increased. However, classical MIMO algorithms either have too high computational complexity or have poor performance. Some existing deep learning-based MIMO detection networks have achieved excellent results in high-order MIMO, but they have unbearable detection performance in low-order MIMO. Based on the idea of dividing the sample space, we propose MLNet. Compared with Det Net and MMNet, MLNet has made a huge leap in time-varying channels. At the same time, MLNet has four orders of magnitude less computation than existing neural network algorithms. We also propose a training method, which is not only useful for MLNet, but also improves the detection performance of DetNet.