Papers › Learnable MIMO Detection Networks Based on Inexact ADMM
Learnable MIMO Detection Networks Based on Inexact ADMM
Minsik Kim, Daeyoung Park
In this article, we present a new iterative MIMO detection algorithm based on inexact alternating direction method of multipliers. Each iteration is considered as a neural network layer with learnable parameters, which are optimized by the stochastic gradient descent algorithm with a training data set of the received vectors and the ground truth transmitted signals. Numerical results show that the proposed algorithm outperforms the existing learnable detection network and it achieves near-optimal performance close to the sphere decoder in the case of a large number of receive antennas.
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