In this paper, we propose a model-driven deep learning network for multiple-input multiple-output (MIMO) detection. The structure of the network is specially designed by unfolding the iterative algorithm. Some trainable parameters are optimized through deep learning techniques to improve the detection performance. Since the number of trainable variables of the network is equal to that of the layers, the network can be easily trained within a very short time. Furthermore, the network can handle time-varying channel with only a single training. Numerical results show that the proposed approach can improve the performance of the iterative algorithm significantly under Rayleigh and correlated MIMO channels. Index Terms-Deep learning, Model-driven, MIMO detection, Iterative algorithm, Neural network This paper develops a model-driven deep learning network for MIMO detection. The structure of this network is obtained by adding some adjustable parameters to the existed iterative detectors and the performance can be improved by deep learning. Our network uses different linear estimators from [15] and more trainable parameters, which renders the network more flexible. Different from these works in [14] , [16] , which are only designed for a fixed channel, our network can perform MIMO detection under time-varying channels. Since few adjustable parameters are required to be optimized, the network can be easily trained within a very short time. Furthermore, this network is very easy to achieve soft decision, which is more suitable for modern wireless communications system. The network is named OAMP-Net as it incorporates deep learning into the orthogonal AMP (OAMP) algorithm. The simulation results show that the OAMP-Net outperforms the OAMP algorithm significantly by learning the optimal parameters from a large number of data. Notations-For any matrix A, A T and tr(A) denote the transpose and the trace of A, respectively. In addition, I is the identity matrix, 0 is the zero matrix, and 1 n is the ndimensional all-ones vector. A proper complex Gaussian with mean µ and covariance Ω can be described by the probability
Similar to DetNet, OAMP-Net REF is designed by unfolding the orthogonal AMP (OAMP) algorithm.
A Model-Driven Deep Learning Network for MIMO Detection
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Computer Science Mathematics