RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection

In this paper, we present a novel neural network architecture for MIMO symbol detection, the Recurrent Equivariant MIMO detector (RE-MIMO). It incorporates several important considerations in wireless communication systems, such as robustness to channel misspecification, the ability to handle a varying number of users with a single model, and invariance to the (sequential) order in which the users interact with the system. The decoder consists of three main blocks; one block to inform the decoder of the channel model, one permutation equivariant block based on the successful transformer architecture to model transmitter channel interactions, and one fully connected feedforward network to predict the demodulated symbols for each user. These blocks are chained together into an iterative decoder that is trained through end-to-end backpropagation. RE-MIMO is compared against a broad range of existing methods and the results confirm the ability of the network to achieve the state of the art demodulation accuracy. In particular, RE-MIMO efficiently handles a variable number of transmitters with only a single trained model, is capable of dealing with correlated channels, and is robust to channel misspecification. In terms of computational complexity, RE-MIMO scales favorably with the number of transmitters.