Deep Mimo Detection Using ADMM Unfolding

Abstract

This paper presents a low-complexity deep neural network (DNN)based multiple-input-multiple-output (MIMO) detector for the BPSK and QPSK constellation cases. We employ deep unfolding, whose idea is to take insight from the structure of an iterative optimization algorithm and attempt to learn a better iterative algorithm. The structure of the network is obtained from an iterative algorithm arising from the application of ADMM to the maximum-likelihood MIMO detection problem. The number of parameters to be learnt in this new design is less than that of DetNet, a recently proposed DNN-based MIMO detector. Our numerical experiments illustrate that the new method outperforms DetNet and several existing MIMO detectors in the large-scale MIMO case. In particular, we show that for a 160×160 MIMO system, our DNN design, with 40 layers, can attain nearly optimal bit-error rate performance.