Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks

In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical applications. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to "model" the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods

Hybrid multi-user equalizer for massive MIMO millimeter-wave dynamic subconnected architecture

This paper proposes a hybrid multi-user equalizer for the uplink of broadband millimeterwave massive multiple input/multiple output (MIMO) systems with dynamic subarray antennas. Hybrid subconnected architectures are more suitable for practical applications since the number of required phase shifters is lower than in fully connected architectures. We consider a set of only analog precoded users transmitting to a base station and sharing the same radio resources. At the receiver end, the hybrid multi-user equalizer is designed by minimizing the sum of the mean square error (MSE) of all subcarriers, considering a two-step approach. In the first step, the digital part is iteratively computed as a function of the analog part. It is considered that the digital equalizers are computed on a per subcarrier basis, while the analog equalizer is constant over the subcarriers and the digital iterations due to hardware constraints. In the second step, the analog equalizer with dynamic antenna mapping is derived to connect the best set of antennas to each radio frequency (RF) chain. For each subset of antennas, one antenna and a quantized phase shifter are selected at a time, taking into account all previously selected antennas. The results show that the proposed hybrid dynamic two-step equalizer achieves a performance close to the fully connected counterpart, although it is less complex in terms of hardware and signal processing requirements.publishe