Model Enhanced Learning Based Detectors (Me-LeaD) for Wideband Multi-User 1-bit mmWave Communications

Referring to the system equipped with single-bit converters, 1-bit mmWave communications is gaining increasing attention for its superb cost efficiency. However, the inherent non-linear distortion renders the detectors designed for classical transparent communications inapplicable, leading to an urgent need for novel detecting solutions dedicated to 1-bit systems. Although a few endeavours have been made towards learning-based (as opposed to the traditional model-based) detectors for multi-user (MU) 1-bit systems, they are exclusively limited to narrowband channels and fail to cope with the multi-path effects inevitable to mmWave systems. In this paper, we first design a learning-based detector (LeaD) for general wideband multi-user (wMU) scenarios. Though stemming from block-based detection, the classic workhorse for transparent systems, LeaD faces either unaffordable complexity or unacceptable data rate in 1-bit systems. Given the impracticability of block-based detection, we resort to the serial detection mechanism and henceforth devise a so-termed model-enhanced (Me-)LeaD by utilizing the channel delay-domain information. Me-LeaD can be further augmented by exploiting the channel angular-domain information. Underpinned by a judiciously tailored method for extracting tbe model information, the proposed Me-LeaD demonstrates a decent overall performance in general 1-bit wMU scenarios.