  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.
