Extrinsic Versus App Information Feedback in Turbo Vep Mu-Mimo Receivers: Optimization Via Deep Unfolding.

The joint use of Soft-Input Soft-Output (SISO) detectors and channel decoders in an iterative manner has received growing attention for Multi-User Multiple-Input Multiple-Output (MU-MIMO) transmission schemes since several years, as it has been shown to operate close to fundamental limits, at least asymptotically. Amongst SISO detectors, message passing algorithms such as Vector Expectation Propagation (VEP) proved to outperform significantly linear detectors such as Linear Minimum Mean Square Error (LMMSE). Aside from its higher computational complexity, turbo VEP receivers rely on different hyper-parameters that can be optimized.In this context, we propose a joint optimization through deep-unfolding of the hyper-parameters that naturally arise in this kind of doubly iterative turbo VEP receivers. One of the difficulties arising for this type of receiver is when and how to choose between an extrinsic or an A Posteriori (APP) information feedback within the turbo receiver. The optimal selection is shown here to depend on the type of the considered SISO components. By properly choosing the hyper-parameters to be optimized, we show that deep-unfolding can naturally optimize the trade-off between extrinsic and APP information feedback and bring performance gains.