RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection

TL;DR

This paper introduces RE-MIMO, a neural network architecture for MIMO detection that is permutation equivariant and adaptable to varying numbers of users, demonstrating high accuracy and efficiency.

Contribution

The paper proposes a novel neural detector architecture that incorporates permutation equivariance and handles variable user counts, advancing neural MIMO detection methods.

Findings

Outperforms existing neural detectors in accuracy.

Handles a variable number of users efficiently.

Exhibits unique properties not seen in prior models.

Abstract

In this paper, we present a novel neural network for MIMO symbol detection. It is motivated by several important considerations in wireless communication systems; permutation equivariance and a variable number of users. The neural detector learns an iterative decoding algorithm that is implemented as a stack of iterative units. Each iterative unit is a neural computation module comprising of 3 sub-modules: the likelihood module, the encoder module, and the predictor module. The likelihood module injects information about the generative (forward) process into the neural network. The encoder-predictor modules together update the state vector and symbol estimates. The encoder module updates the state vector and employs a transformer based attention network to handle the interactions among the users in a permutation equivariant manner. The predictor module refines the symbol estimates. The modular and permutation equivariant architecture allows for dealing with a varying number of users. The resulting neural detector architecture is unique and exhibits several desirable properties unseen in any of the previously proposed neural detectors. We compare its performance against existing methods and the results show the ability of our network to efficiently handle a variable number of transmitters with high accuracy.